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Table of Content

Welcome

QuantConnect is an open-source, community-driven algorithmic trading platform. Our trading engine is powered by LEAN , a cross-platform, multi-asset technology that brings cutting-edge finance to the open-source community. We support Python and C# programming languages.

Our Mission

Quantitative trading infrastructure should be open-source. Millions of financial engineers rewrite the same infrastructure and then keep their work closed to make it harder for competing trading firms. We take a radically open approach to quant finance and let our users focus on alpha, not infrastructure.

The future of finance is automated and we are the open-source infrastructure to power this future. Firms choose our open-source platform as it provides a 10-100x improvement in time to market and substantially reduces the risk of developing your quantitative tools. As our brokerage and data integrations expand, this leverage will be more exceptional.

Everyone should have access to quantitative finance. Algorithmic trading is a powerful tool and we want to open it up to all investors. We do this with transparent, scalable pricing that allows all investors to access quantitative finance. For more information on our mission, check out our Manifesto .

Data Library

We provide an enormous library of data for your backtesting, research, and live trading. The library includes data for Equities, Options, Futures, CFDs, Forex, Crypto, Indices, and alternative data. The library is roughly 400TB in size, contains trade data that spans decades into the past, and comes in tick to daily resolutions. View the Dataset Market to see all of the datasets that we have available, including their respective start dates, end dates, and resolutions. The following image shows the integrated data providers:

Business Model

QuantConnect provides cloud infrastructure as a service, similar to many cloud compute vendors. We encourage quants and start up firms to grow within our ecosystem to keep our pricing accessible to individuals and small firms.

For companies interested in running QuantConnect on-premise, we can install it within your corporate firewall and help you get set up with financial data. We charge a set up and maintenance fee for these installations.

Getting Started

GET STARTED WITH QUANTCONNECT

Guide through creating a project, running your first backtest, and live algo trading.

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Follow these steps to create, backtest, and paper trade a new algorithm:

1. Log in to the Algorithm Lab .

2. On the Projects page, click Create New Algorithm .

3. A new project opens in the Cloud Integrated Development Environment (IDE) .

4. At the top of the IDE, click the Build icon.

5. At the top of the IDE, click the Backtest icon.

The backtest results page displays your algorithm’s performance over the backtest period.

6. At the top of the IDE, click the Deploy Live icon.

7. On the Deploy Live page, click the Brokerage field and then click Paper Trading from the drop-down menu.

8. Click Deploy .

The live results page displays your algorithm’s live trading performance.

To deploy a live algorithm with a different brokerage, see the Deploy Live Algorithms section of the brokerage integration documentation .

Security and IP

Introduction

You own all your intellectual property and your code. Your code is private by default unless you explicitly share it with the community in the forums or with another team member via collaboration. You are creating valuable intellectual property, and we respect this and wish to make it easier. We limit the QuantConnect staff members who have access to the database. If we ever need access to your algorithm for debugging, then we will explicitly request permission first.

Intellectual Property

The following sections explain our commitment to protecting your intellectual property.

Ownership

You own all your intellectual property and your code. Your code is private by default unless you explicitly share it with the community in the forums or with another team member via collaboration. You are creating valuable intellectual property; we respect this and wish to make it easier. We document this publicly in our Terms of Service .

Alignment

Beyond words and our legal terms of service, we live this through the alignment of our business model with you, our clients. We aim for you to become more successful, thereby growing within our ecosystem. We've served start-up quant funds as they've grown from $0 to $1B+ AUM.

Reputation and Track Record

Established in 2012, we have a pristine reputation and a 10+ year record of protecting our community's intellectual property. We have served more than 200,000 clients. If we were to violate the trust of even a single client, we'd lose the entire community's faith - it is simply not worth it. We seek to change the future of finance and are driven by this mission.

Sharing

QuantConnect provides support with a specific support agent, not an anonymous team. When submitting a support ticket, you explicitly grant that team member access to your project. You can remove the support agent from your project collaboration at any time.

Security

We take a multi-level approach to security, from physical security to digital and information systems, internal processes, and testing.

Physical Security of Servers

Physical access to our servers is limited to a few dedicated team members whom QuantConnect has vetted. Only those credentialed team members can access the physical servers, and we schedule all work in advance. Work on the servers is always done in pairs to prevent single rogue actors from accessing the servers. We host our servers in a world-class security facility (Equinix) with security staff 24/7.

Information and Digital Security

We use all good common sense information security processes: passwordless servers, encryption in the database and backups, encrypted traffic, and network monitoring. We keep most of our servers off the internet and only available on private networks for the smallest possible surface area. We have regular network and code penetration testing. All code is containerized and isolated in services so that root network access would provide little-to-no benefit.

Beyond these basics, we've built active monitoring technology that proactively detects and blocks threats. We have human detection services to reduce the chances of brute-force attacks. We have documented processes for client notifications in the event of strange network activity.

Processes

Deployment environments are automated and enforce code peer-review to be deployed, reducing the chances of a rogue internal agent.

We limit staff access to the physical servers, restricting core database access to only a handful of senior staff. Database credentials are carefully restricted in scope, access locations and frequently rotated.

Privacy

Protecting users' private and intellectual property is of utmost importantance to us. QuantConnect complies with GDPR and all relevant privacy laws. We will never sell or publish your email address. We request knowledge of your real identity to ensure compliance with our data licenses but accept using an alias on public profiles for privacy. For more information about what data we collect, which tracking technologies we use, and how we use and share your data, see our Privacy Policy .

Code Encryption

When the above processes and track record are insufficient, we offer ways to encrypt your code while working on the QuantConnect platform. Our encryption feature uses a locally hosted key to encrypt code at rest in our database with AES256 encryption. In case of a compromised database or rouge agent, your code would not be readable. For more information on this feature, see Project Encryption .

On-Premise Installations

QuantConnect technology can be installed fully on-premise, fulfilling the requirements of even the strictest compliance departments. This technology, called the Local Platform, provides the same user interface as our cloud environment with support for fully offline "anonymous" project development. For more information on this feature, see the Local Platform .

Organizations

Organizations > Getting Started

Introduction

An organization is a collection of members that share hardware resources, share access to datasets, and collaborate together to develop projects. Hardware resources are used to run backtests, launch research notebooks, deploy live trading algorithms, and store project data. You can create new organizations and join existing ones. You can be a member in any number of organizations. We offer several organization tiers so you can tailor your team's subscriptions as you grow over time. For the times when you need access to a QuantConnect engineer to help solve development issues, assign support seats among your team. There are several tiers of support seats to match the level of support your team requires.

Add Organizations

Follow these steps to add new organizations to your profile:

1. Log in to the Algorithm Lab.
2. In the top navigation bar, click Connected as: organizationName .
3. In the Switch Organization panel, click Create Organization .
4. Enter the organization name and then click Add .

The organization name must be unique. "Created Successfully" displays.

Switch Organizations

Follow these steps to switch organizations:

1. Log in to the Algorithm Lab.
2. In the top navigation bar, click Connected as: organizationName .
3. In the Switch Organization panel, click the name of the organization for which you want to connect.

The top navigation bar displays the new organization name.

Rename Organizations

Follow these steps to change your organization name:

1. Open the organization homepage .
2. Hover over the organization name and then click the pencil icon that appears.

3. Enter the new organization name and then click Save Changes .

"Organization Name Updated Successfully" displays.

Get Organization Id

To get the organization Id, open Organization > Home and check the URL. For example, the organization Id of https://www.quantconnect.com/organization/5cad178b20a1d52567b534553413b691 is 5cad178b20a1d52567b534553413b691.

Out of Sample Period

To reduce the chance of overfitting, organization managers can enforce all backtests must end a certain number of months before the current date. For example, if you set a one year out-of-sample period, the researchers on your team will not be able to use the most recent year of data in their backtests. A out-of-sample period is helpful because it leaves you a period to test your model after your done the development stage. Follow these steps to change the backtest out-of-sample period:

1. Open the organization homepage .
2. Scroll down to the Backtesting Out of Sample Period section.
3. Adjust the out-of-sample period duration or click on "No Holdout Period".

Organizations > Tier Features

Introduction

An organization is a collection of members that share hardware resources, share access to datasets, and collaborate together to develop projects. Hardware resources are used to run backtests, launch research notebooks, deploy live trading algorithms, and store project data. You can create new organizations and join existing ones. You can be a member in any number of organizations. We offer several organization tiers so you can tailor your team's subscriptions as you grow over time. For the times when you need access to a QuantConnect engineer to help solve development issues, assign support seats among your team. There are several tiers of support seats to match the level of support your team requires.

Organizations let you coordinate resources and teamwork on QuantConnect Cloud. There are 5 tiers of organizations and each tier has its own set of features. Each account starts with a personal organization on the Free tier with access to one free backtest node and one free research node. However, to accommodate the growth of your trading skills and business, you can adjust the tier of your organization at any time. Higher tiers offer more live nodes to run more live algorithms, more backtesting nodes for faster concurrent backtesting, and many other features.

Free Tier

The Free tier provides cloud access to datasets for all of the asset classes in our Datasets Market . The free data ranges from minute to daily resolutions and can be used to either run backtests or perform analysis in the Research Environment . When backtesting, Free organizations have access to our built-in auto-complete and debugging features in the web IDE. After a successful backtest, Free organizations can use our report generator to create professional-grade reports that reflect their backtest performance. Free organizations have access to our online documentation, community forum, YouTube channel, and Learning Center.

Quant Researcher Tier

The Quant Researcher tier is designed for self-directed investors, students, academics, and independent traders seeking to manage their personal portfolio. We recommend the Quant Researcher Pack to make the most of QuantConnect.

The Quant Researcher tier builds on the features included in the Free tier. Organizations on the Quant Researcher tier have access to the QuantConnect API and can use the CLI to run Lean locally. When members in these organizations need assistance from a QuantConnect engineer, support seats are available to request private support . Members within Quant Researcher organizations that have the required permissions can adjust the resources within the organization.

In Quant Researcher organizations, members can use second and tick resolution data from the Datasets Market. There is no limit on the number of projects these organizations can hold. They can produce up to 100KB of logs/backtest, 3MB of logs/day. 10 million orders/backtest, and can have up to two backtesting nodes to run up to two concurrent backtests. Members in these organizations can have up to two active coding sessions in the organization. After a successful backtest, members in these organizations can use parameter optimization tools to improve the performance of their backtest. When the members are ready to deploy strategies live, Quant Researcher organizations can subscribe to up to 2 live trading nodes to unlock live trading with real or paper money. Each live algorithm in a Quant Research organization can send up to 20 Telegram, Email, or Webhook notifications per hour for free. SMS notifications and additional Telegram, Email, or Webhook notifications require QuantConnect Credit (QCC).

Team Tier

The Team tier is designed for sophisticated individuals and teams of quant collaborators such as Quant Start Ups, Fintech Companies, and Emerging Managers. We recommend the Team Pack to make the most of QuantConnect.

The Team tier expands on the features included in the Quant Researcher tier. Organizations on the Team tier can have up to 10 members and the members can collaborate on projects together. These organizations can produce 1MB of logs/backtest, 10MB of logs/day, and there is no limit on the number of orders that they can place in backtests. Organizations on the Team tier can have up to 10 backtesting nodes, 10 research nodes, and 10 live trading nodes. Members in these organizations can have up to four active coding sessions in the organization.

To accommodate a large number of projects in Team organizations, these organizations can expand the capacity of their Object Store up to 10GB. Annual contracts for onboarding services are available on request to get teams operational in the shortest amount of time. When live trading, Team organizations have more options than the lower tiers because both the Trading Technologies brokerage and our live Futures data provider are available. Each live algorithm in a Team organization can send up to 60 Telegram, Email, or Webhook notifications per hour for free.

Trading Firm Tier

The Trading Firm tier is designed for growing quantitative firms, prop desks, hedge funds, ETF companies, professional teams of quants, and sophisticated independent investors. It has special features for collaborating with consultants to protect the investor IP. If you are a company on QuantConnect, we recommend the Trading Firm Pack to make the most of QuantConnect.

The Trading Firm tier builds on the features included in the Team tier. Organizations on the Trading Firm tier can have an unlimited number of members and an unlimited number of collaborators simultaneously working on individual projects. The IP ownership of all the projects in these organizations remains within the organization. There is no limit on the number of backtesting, research, and live trading nodes these organizations can rent. They can produce 5MB of logs/backtest and 50MB of logs/day. Members in these organizations can have up to eight active coding sessions in the organization. Each live algorithm in a Trading Firm organization can send up to 240 Telegram, Email, or Webhook notifications per hour for free.

The owner of a Trading Firm organization can grant various permissions to the organization’s members, including designating a member to manage the organization's billing. These organizations have access to custom lean builds, so they can use feature branches or historical master branches to run their strategies. An example of this could be granting only a few members of your team live trading deployment access.

In addition to the brokerages and data providers available to Team organizations, Trading Firm organizations can use Interactive Brokers Financial Advisor accounts to manage sub-accounts for clients.

Institution Tier

The Institution tier is designed for established larger funds, large prop desks, hedge funds, banks, ETF vehicles, and professional teams of quants. It is "unlocked", so you can run it on premise to serve your internal teams. If this sounds interesting, reach out and we'd be happy to arrange a demonstration for your department.

The Institution tier builds on the features included in the Trading Firm tier. Organizations on the institutional tier have no limit on the number of backtest logs that they can produce, and each member of the organization can have up to 16 active coding sessions. These organizations can use Terminal Link to live trade Equities, Futures, and Options via the Bloomberg EMSX. They can also request custom libraries and frameworks to use in the QuantConnect web IDE and receive instant messaging support from a QuantConnect engineer. Each live algorithm in an Institution organization can send up to 3,600 Telegram, Email, or Webhook notifications per hour for free.

Institutional clients can run our proprietary build of LEAN, LEAN Enterprise, on-premise. LEAN Enterprise holds speed improvements we haven't pushed to the open-source.

Organizations > Resources

Introduction

Organizations can subscribe to hardware resources to run backtests, launch research notebooks, and deploy live trading algorithms to co-located servers. Organizations also have access to storage resources via the Object Store to store data between backtests or live trading deployments. To promote efficiency, all of these resources within your organization are shared among all of the members within the organization. A team of several quants can all share one backtest, research, and live trading node.

Backtesting Nodes

Backtesting nodes enable you to run backtests. The more backtesting nodes your organization has, the more concurrent backtests that you can run. Several models of backtesting nodes are available. Backtesting nodes that are more powerful can run faster backtests and backtest nodes with more RAM can handle more memory-intensive operations like training machine learning models, processing Options data, and managing large universes. The following table shows the specifications of the backtesting node models:

Refer to the Pricing page to see the price of each backtesting node model. You get one free B-MICRO backtesting node in your first organization. This node incurs a 20-second delay when you launch backtests, but the delay is removed and the node is replaced when upgrade your organization to a paid tier and add a new backtesting node .

To view the status of all of your organization's nodes, see the Resources panel of the IDE. When you run a backtest, it uses the best-performing resource by default, but you can select a specific resource to use .

The CPU nodes are available on a fair usage basis while the GPU nodes can be shared with a maximum of three members. Depending on the server load, you may use all of the GPU's processing power. GPU nodes perform best on repetitive and highly-parallel tasks like training machine learning models. It takes time to transfer the data to the GPU for computation, so if your algorithm doesn't train machine learning models, the extra time it takes to transfer the data can make it appear that GPU nodes run slower than CPU nodes.

You can't use backtesting nodes for optimizations .

Research Nodes

Research nodes enable you to spin up an interactive, command-line, Jupyter Research Environment . Several models of research nodes are available. More powerful research nodes allow you to handle more data and run faster computations in your notebooks. The following table shows the specifications of the research node models:

Refer to the Pricing page to see the price of each research node model. You get one free R1-4 research node in your first organization, but the node is replaced when you subscribe to a paid research node in the organization.

To view the status of all of your organization's nodes, see the Resources panel of the IDE. When you launch the Research Environment, it uses the best-performing resource by default, but you can select a specific resource to use .

The CPU nodes are available on a fair usage basis. The GPU nodes can be shared with a maximum of three members. Depending on the server load, you may use all of the GPU's processing power.

Live Trading Nodes

Live trading nodes enable you to deploy live algorithms to our professionally-managed, co-located servers. You need a live trading node for each algorithm that you deploy to our co-located servers. Several models of live trading nodes are available. More powerful live trading nodes allow you to run algorithms with larger universes and give you more time for machine learning training . Each security subscription requires about 5MB of RAM. The following table shows the specifications of the live trading node models:

Refer to the Pricing page to see the price of each live trading node model.

To view the status of all of your organization's nodes, see the Resources panel of the IDE. When you deploy an algorithm, it uses the best-performing resource by default, but you can select a specific resource to use .

The CPU nodes are available on a fair usage basis while the GPU nodes can be shared with a maximum of two members. Depending on the server load, you may use all of the GPU's processing power. GPU nodes perform best on repetitive and highly-parallel tasks like training machine learning models. It takes time to transfer the data to the GPU for computation, so if your algorithm doesn't train machine learning models, the extra time it takes to transfer the data can make it appear that GPU nodes run slower than CPU nodes.

Sharing Resources

Your organization's nodes are shared among all of the organization's members to reduce the amount of time that nodes idle. In the Algorithm Lab, you can see which nodes are available within your organization.

Node Quotas

The following table shows the number of nodes each organization tier can have:

Training Quotas

Algorithms normally must return from the OnData on_data method within 10 minutes, but the Train train method lets you increase this amount of time. Training resources are allocated with a leaky bucket algorithm where you can use a maximum of n-minutes in a single training session and the number of minutes available refills over time. This gives you a reservoir of training time when you need it and recharges the reservoir to prepare for the next training session. The reservoir only starts draining after you exceed the standard 10 minutes of training time.

The following animation demonstrates the leaky bucket algorithm. The tap continuously adds water to the bucket. When the bucket is full, water spills over the rim of the bucket. The water represents your training resources. When your algorithm exceeds the 10 minutes of training time, holes open at the bottom of the bucket and water begins to drain out. When your algorithm stops training, the holes close and the bucket fills up with water.

The following table shows the amount of extra time that each backtesting and live trading node can spend training machine learning models:

The refill rate in the table above is based on the real-world clock time, not the backtest clock time. In backtests, the Train train method is synchronous, so it will block your algorithm from executing while the model is trained. In live trading, the method runs asynchronously, so ensure your model is ready to use before you continue executing the algorithm. Training occurs on a separate thread, so use a semaphore to track the model state.

Log Quotas

Per our Terms and Conditions , you may not use the logs to export dataset information. The following table shows the amount of logs each organization tier can produce:

To check the log storage space you have remaining, log in to the Algorithm Lab and then, in the left navigation bar, click Organization > Resources .

If you delete a backtest or project that produced logs, your quotas aren't restored. Additionally, daily log quotas aren't fully restored at midnight. They are restored according to a 24-hour rolling window.

The log files of each live trading project can store up to 1,000,000 lines for up to two years. If you log more than 1,000,000 lines or some lines become older than two years, we remove the oldest lines in the files so your project stays within the quota.

To avoid reaching the limits, we recommend logging sparsely, focusing on the change events instead of logging every time loop. You can use the debugger to inspect objects during runtime. If you use the debugger, you should rarely reach the log limits.

Coding Session Quotas

If you have a project open, it uses a coding session. Paid organizations can have multiple active coding sessions, but free users can only have one coding session open at a time. The following table shows how many active coding sessions you can have on each organization tier:

If the organization you're in has more live trading nodes than your initial coding session quota, then your coding session quota increases to the number of live trading nodes you have in the organization so you can view all your live strategies.

The quota for free organizations is a global quota, so you can have one active coding session across all of your free organizations. The quotas for paid organizations are at the organization level. Therefore, if you are in two Quant Researcher organizations, you can have two active coding sessions in one of those organizations and another two active sessions in the other organization. These paid tier quotas are for each account, not for the organization as a whole. For instance, a Trading Firm organization can have more than eight members and all of the members can simultaneously work on projects within the organization.

File Size Quotas

The maximum file size you can have in a project depends on your organization's tier. The following table shows the quota of each tier:

Live Trading Notification Quotas

The number of email, Telegram, or webhook notifications you can send in each live algorithm for free depends on the tier of your organization. The following table shows the hourly quotas:

If you exceed the hourly quota, each additional email, Telegram, or webhook notification costs 1 QuantConnect Credit (QCC).

Each SMS notification you send to a US or Canadian phone number costs 1 QCC. Each SMS notification you send to an international phone number costs 10 QCC.

View All Nodes

The Resources page displays your backtesting, research, and live trading node clusters. To view the page, log in to the Algorithm Lab and then, in the left navigation bar, click Organization > Resources .

To toggle the format of the page, click the buttons in the top-right. If the page is in table view, each cluster section includes a table with the following columns:

Add Nodes

You need billing permissions in the organization to add nodes.

Follow these steps to add nodes to your organization:

1. Open the Resources page.
2. Click Add nodeType Node for the type of node you want to add.
3. Select the node specifications.
4. Click Add Node .

The Resources page displays the new node.

Remove Nodes

You need billing permissions in the organization to remove nodes. If you remove nodes during your billing period, your organization will receive a pro-rated credit on your account, which is applied to future invoices.

Follow these steps to remove nodes from your organization:

1. Log in to the Algorithm Lab.
2. In the left navigation bar, click Organization > Home .
3. On the organization homepage, click Edit Plan .
4. Click the Customize Plan > Build Your Own Pack > Compute Nodes tab.
5. Click the minus sign next to the node model you want to remove.
6. Click Proceed to Checkout .
   

Rename Nodes

We assign a default name to hardware nodes that includes the model name and an arbitrary string of characters, but you can follow these steps to rename the nodes in your organization:

1. Open the Resources page.
2. Click Set Name on the node that you want to rename.
3. Enter the new node name and then click Save Changes .

The Resources page displays the new node name.

Stop Nodes

You need stop node permissions in the organization to stop nodes other members are using. If you stop a node, it terminates the running backtest, research, or live trading sessions. When you stop a live trading node, the portfolio holdings don't change but the algorithm stops executing.

Follow these steps to stop nodes that are running in your organization:

1. Open the Resources page.
2. Click the icon with the three horizontal lines icon in the top-right corner to format the page into table view.
   

3. Click Stop in the row with the node that you want to stop.

Organizations > Object Store

Introduction

The Object Store is an organization-specific key-value storage location to save and retrieve data in QuantConnect's cache. Similar to a dictionary or hash table, a key-value store is a storage system that saves and retrieves objects by using keys. A key is a unique string that is associated with a single record in the key-value store and a value is an object being stored. Some common use cases of the Object Store include the following:

• Transporting data between the backtesting environment and the research environment.
• Training machine learning models in the research environment before deploying them to live trading.

The Object Store is shared across the entire organization. Using the same key, you can access data across all projects in an organization.

View Storage

The Object Store page shows all the data your organization has in the Object Store. To view the page, log in to the Algorithm Lab and then, in the left navigation bar, click Organization > Object Store .

To view the metadata of a file (including it's path, size, and a content preview), click one of the files in the table.

Upload Files

Follow these steps to upload files to the Object Store:

1. Open the Object Store page.
2. Navigate to the directory in the Object Store where you want to upload files.
3. Click Upload .
4. Drag and drop the files you want to upload.

Alternatively, you can add data to the Object Store in an algorithm or notebook .

Download Files

Permissioned Institutional clients can build derivative data such as machine learning models and download it from the Object Store. Contact us to unlock this feature for your account.

Follow these steps to download files and directories from the Object Store:

1. Open the Object Store page.
2. Navigate to the directory in the Object Store where you want to download files and directories.
3. Select the file(s) and directory(ies) to download
4. Click Download .
5. Wait while QuantConnect processes the request.
6. Click the Download link that appears.

Storage Sizes

All organizations get 50 MB of free storage in the Object Store. Paid organizations can subscribe to more storage space. The following table shows the cost of the supported storage sizes:

Delete Storage

Follow these steps to delete storage from the Object Store:

1. Open the Object Store page.
2. Navigate to the directory in the Object Store where you want to delete files.
3. Click the check box next to the files you want to delete.
4. Click Actions and then click Delete from the drop-down menu.
5. Click OK .

Alternatively, you can delete storage in an algorithm or notebook .

Edit Storage Plan

You need storage billing permissions and a paid organization to edit the size of the organization's Object Store.

Follow these steps to edit the amount of storage available in your organization's Object Store:

1. Log in to the Algorithm Lab.
2. In the left navigation bar, click Organization > Resources .
3. On the Resources page, scroll down to the Storage Resources and then click Add Object Store Capacity .
4. On the Pricing page, select a storage plan.
5. Click Proceed to Checkout .

Research to Live Considerations

When you deploy a live algorithm, you can access the data within minutes of modifying the Object Store. Ensure your algorithm is able to handle a changing dataset.

The live environment's access to the Object Store is much slower than in research and backtesting. Limit the individual objects to less than 50 MB to prevent live trading access issues.

Usage by Project

The Resources page shows the total storage used in your organization and the storage used by individual projects so that you can easily manage your storage space. To view the page, log in to the Algorithm Lab and then, in the left navigation bar, click Organization > Resources .

Organizations > Support

Introduction

The community is a great resource for support for developing algorithms. However, for more personalized assistance and privacy, you can submit support tickets to request assistance from a QuantConnect engineer. Premium support allows you to share your algorithms with IP protection and enables the Support Team to address issues with live algorithms. In order to submit a support ticket, you must have a support seat in your organization.

There are three tiers of support seats and each tier provides different services. You can file support tickets to get private assistance with issues, but support tickets should not replace your efforts of performing your own research and reading through the documentation. If you need further assistance than what our Support Team offers, consider hiring an Integration Partner .

Features

The services our Support Team provides depend on the tier of your support seat.

Bug Reports

The Lean trading engine is under constant development, so you may occasionally experience bugs when using it. If you think you have found a bug, share a simple backtest with us that reproduces the issue. You can contact us either through email , Discord , or the forum . We will review your submission. If we confirm you've found a bug, we will create a GitHub Issue to have it resolved. Subscribe to the GitHub Issue to track our progress in fixing the bug.

Feature	Community	Bronze	Silver	Gold
Bug Reports

AI Support

Mia is an AI assistant we trained on hundreds of algorithms and thousands of documentation pages to provide contextual assistance for most issues you may encounter when developing a strategy.

Feature	Community	Bronze	Silver	Gold
AI Support

Email Support

Our support ticket system enables you to privately email with our Support Team. We address support tickets in a first-in, first-out order, but we give priority to tickets opened by members with higher support seats. The following table shows our response time for each of the support tiers:

Feature	Community	Bronze	Silver	Gold
Email Support	-

IP Protection

If you attach a backtest or live trading deployment when you open a support ticket , the intellectual property of your project is protected. We have a restricted subset of the Support Team who can access private support tickets. Using paid support plans ensures only a limited subset of the QuantConnect team can access the algorithms you attach to support tickets. These team members are carefully selected, have been at QuantConnect for at least 2 years, and have passed a background check. In contrast, if you share a backtest or live trading deployment to the forum for assistance, your project becomes part of the public domain.

Feature	Community	Bronze	Silver	Gold
IP Protection	-

Live Trading Debugging

Paid support plans have access to our live deployment debugging service. If you experience an issue with a live trading deployment, open a support ticket. We can assist with uncovering the issue, fixing the issue, and getting you ready to redeploy the algorithm. We can't assist with live trading issues in the community forum or Discord.

Feature	Community	Bronze	Silver	Gold
Live Trading Debugging	-

Algorithm Design Suggestions

If you have a silver or gold support seat, we can offer suggestions on the design of your algorithms. Our Support Team members are experts on the inner workings of Lean, so we can guide you on improving the efficiency of your algorithms by following our common design patterns. We can usually reduce the size of your project's code files and increase the speed of your project backtesting.

Feature	Community	Bronze	Silver	Gold
Algorithm Design Suggestions	-	-

Private Chatroom

When you require instant access to our Support team, we can open a private chatroom in Discord. In our private chatroom, you can ask our Support Team questions at any time and we focus on responding as quickly as we can. Request a private chatroom to avoid waiting for email responses on your support tickets.

Feature	Community	Bronze	Silver	Gold
Private Chatroom	-	-	-

Phone Call Consultations

We offer phone support to members with gold support seats. You can take advantage of our phone support for up to 1 hour per month. During phone calls, feel free to ask about anything related to QuantConnect, Lean, or quant trading. We recommend planning your questions before you call in order to best utilize the time available.

Feature	Community	Bronze	Silver	Gold
Phone Call Consultations	-	-	-

Local Development

If you have a gold support seat, you can access technical support for the Local Platform , LEAN CLI , and the QuantConnect REST API , which includes installation and subjects that don't apply to the Cloud Platform.

Feature	Community	Bronze	Silver	Gold
Private Chatroom	-	-	-

Summary

The following table shows the features available in each tier:

Feature	Community	Bronze	Silver	Gold
Bug Reports
AI Support
Email Support	-
IP Protection	-
Live Trading Debugging	-
Algorithm Design Suggestions	-	-
Private Chatroom	-	-	-
Phone Call Consultations	-	-	-
Local Development	-	-	-

View All Seats

The Team Management page displays the support seat assignments within your organization. To view the page, log in to the Algorithm lab, and then in the left navigation bar, click Organization > Members .

Add Seats

Follow these steps to assign support seats to members in your organization:

1. Open the Team Management page.
2. Scroll down to the Organization Support section and then click Add Seat .
3. On the Support page, click Select under the tier of seat you want to assign.
4. In the Assign seatTier Seat section, click the team member field and then select the team member you want to assign the seat to from the drop-down menu.
5. Click Add Seat +$ price .

View All Tickets

The Support History page displays all of your organization's support tickets. To view the page, log in to the Algorithm Lab and then, in the left navigation bar, click Support > Organization Tickets .

To see all closed tickets, click Closed . To view the conversation history with our Support Team regarding a ticket, click a ticket.

Open New Tickets

Follow these steps to open support tickets:

1. Log in to the Algorithm Lab.
2. In the left navigation bar, click Support .
3. On the Support page, enter a subject and message.
4. If you want to attach a live deployment or backtest to the support ticket, follow these steps:
1. Click Add Project , select a project, and then click OK .
2. (Optional) Click Add Live Deployment , select a live deployment, and then click OK .
3. (Optional) Click Add Backtest , select a backtest, and then click OK .
5. Click the By submitting this request, I give QuantConnect Support Staff permission to view my project check box.
6. Click Send .

Mia, our AI assistant, provides an initial response to support tickets. We trained it on hundreds of algorithms and thousands of documentation pages to provide contextual assistance for most issues you may encounter when developing a strategy. To escalate the ticket to a human on the Support Team, click No, I'd like a human response .

Comment on Tickets

Follow these steps to comment on support tickets:

1. Open the Support History page.
2. If the ticket you want to comment on is closed, click Closed .
3. Click the ticket on which you want to comment.
4. Enter your comment and then click Send .

Close Tickets

Follow these steps to close support tickets:

1. Open the Support History page.
2. Click the ticket you want to close.
3. (Optional) Enter a reason for closing the ticket.
4. Click Send and Close Ticket .

Open Closed Tickets

Follow these steps to open closed support tickets:

1. Open the Support History page.
2. Click Closed .
3. Click the ticket that you want to open.
4. (Optional) Enter a reason for opening the ticket.
5. Click Open Ticket and Send .

Hours of Operation

The human Support Team operates Monday to Friday. Mia, our AI support agent, operates 24/7.

AI Support

Mia is an AI assistant we trained on hundreds of algorithms and thousands of documentation pages to provide contextual assistance for most issues you may encounter when developing a strategy. Mia automatically provides an inital response to the support tickets you open. You can also interact with Mia through the community forum , Discord , or the Algorithm Lab Console .

Ticket Quotas

The following table shows the number of tickets each seat tier can open during a 1-month rolling period:

The quotas don't restore on the first day of the month or the billing date. They restore according to a 1-month rolling window.

Organizations > Members

Introduction

Organizations consist of members and members can be part of multiple organizations. The number of members your organization can have depends on the tier of your organization. All members in your organization can access the resources within the organization.

Members can collaborate with other team members by adding them to projects they create. Otherwise, members don't have access to projects in your organization.

If you are the manager of a Trading Firm or Institution organization, you can grant additional permissions to your team members.

View All Seats

You need a seat for each member in your organization.

To see the number of team seats you have in your organization, open the Billing page and then scroll down to the Products section.

Add Seats

You need billing permissions within an organization to add team seats.

Follow these steps to add team seats to your organization:

1. Open the organization homepage .
2. Click Edit Plan .
3. Click the Customize Plan > Build Your Own Pack > Organization Seats tab.
4. In the organizationTier Seats section, click the plus icon.
5. Click Proceed to Checkout .

View All Members

The Team Management page displays all of the members in your organization. To view the page, log in to the Algorithm Lab and then, in the left navigation bar, click Organization > Members .

To toggle the format of the page, click the buttons in the top-right.

Membership Quotas

The number of members your organization can have depends on the organization's tier . In general, higher organization tiers can have more members within the organization to share resources. This design enables you to upgrade your organization as your trading business grows over time. The following table shows the number of members each organization tier can have:

Add Members

You need vacant team seats and team add permissions within an organization to add team members.

Follow these steps to add team members to your organization:

1. Open the Team Management page.
2. Click Add Member .
3. Enter the email address of the new team member and then click Add Member .

The email you enter should be the email the new member uses to log in to their QuantConnect account.

4. If you have team edit permissions, select the permissions for the new member and then click Save Changes . Otherwise, click Cancel .

The Team Management page displays and the new member is included.

On the Add Member dialog you can generate a unique link to invite team members to your organization. Anyone with this link can join your organization.

The new member must switch organization to access its resources and collaborate.

Add Support

Follow these steps to assign support seats to members in your organization:

1. Open the Team Management page.
2. Scroll down to the Organization Support section and then click Add Seat .
3. On the Support page, click Select under the tier of seat you want to assign.
4. In the Assign seatTier Seat section, click the team member field and then select the team member you want to assign the seat to from the drop-down menu.
5. Click Add Seat +$ price .

Remove Members

You need team removal permission to remove members from your organization. Before removing members, stop all nodes they are using. Removing members is not reversible and the members will lose access to the organization's projects. On the Team tier, projects that the members created in the organization will remain with the members. On the Trading Firm and Institution tiers, projects that the members created in the organization will be transferred to the organization manager.

Follow these steps to remove members from your organization:

1. Open the Team Management page.
2. If the Team Management page is in tile view, follow these steps:
1. Click the three dots icon in the top-right corner of the member you want to remove and then click Remove .

2. Click Remove .
3. If the Team Management page is in table view, follow these steps:
1. Click Remove next to the member you want to remove from the organization.

2. Click Remove .

Permissions

Each member of your organization has access to the resources within the organization. If you are the manager of a Trading Firm or Institution organization, you can grant additional permissions to your team members. There are several categories of permissions.

Billing Permissions

The following table shows the supported billing permissions:

Stop Node Permissions

The following table shows the supported node permissions:

To stop active nodes, see Stop Nodes .

Team Permissions

The following table shows the supported team permissions:

Storage Permissions

The following table shows the supported team permissions:

Edit Permissions

You need team edit permission to edit the permissions of team members.

Follow these steps to edit team permissions:

1. Open the Team Management page.
2. If the Team Management page is in tile view, follow these steps:
1. On the Team Management page, click the three dots in the top-right corner of the member you want to edit and then click Edit Permissions .

2. Select and deselect permissions as desired and then click Save Changes .
3. If the Team Management page is in table view, follow these steps:
1. On the Team Management page, click Edit Permissions next to the member whose permissions you want to edit.

2. Select and deselect permissions as desired and then click Save Changes .

Organizations > Administration

Introduction

You can view and manage your organizations from the Algorithm Lab. The algorithms you store in the Algorithm Lab are secure and you maintain their intellectual property.

Intellectual Property

All individuals on QuantConnect own their intellectual property (IP) on our platform. Your code is private and only accessible by people you share the project with and with support-engineers when you submit a support ticket. At no point does QuantConnect ever claim ownership of user IP. The only case where algorithm code becomes public domain is when they are shared to the forum. In this case, algorithms need to be made public domain to allow the sharing of the algorithm code.

It is common when companies hire engineers to write software, they require their employees to sign an agreement that gives the company IP ownership of any code written for work. They need this because they're paying you to write software, and the company needs to sell that software to turn a profit. Similarly, the Organizations feature allows you to control who holds IP ownership over a project. Each type of organization has its own mechanisms for handling project IP ownership.

Individual Organizations

The Free and the Quant Researcher tiers only allow single-member organizations. This means you can't collaborate with anyone else inside the QuantConnect platform. Simply put, you own the IP for any projects you work on since you are the sole collaborator.

Team Organizations

For organizations that allow multiple users to collaborate on projects, the user who created the project owns it; this can be you or one of your teammates. If you add a teammate/collaborate, they can clone it, but the original project belongs to the person who first created it.

Trading Firm & Institution Organizations

For Trading Firm and Institution organizations, which are generally used by companies and funds, the firm owns all employee projects. This is made to suit firms that wish to hire consultants and need to ensure the code remains with the company when the consultant work is finished. You have to explicitly create a project in an organization for it to be created on the organization's account.

Corporate Branding

You can customize your organization’s image, name, and description in the Algorithm Lab to match your branding. If you have a Trading Firm or Institution organization, you can integrate the Algorithm Lab into your website so that your company logo is in the navigation bar and the color matches your website's theme.

Migrating Projects

If you are the administrator of an organization, you can migrate a project out of the organization to another organization in which you’re a member. When your project is migrated, the project files are copied but the content stored in the Object Store is not retained.

View the Organization Homepage

The organization homepage displays a summary of your organization. To view the page, log in to the Algorithm Lab and then, in the left navigation bar, click Organization > Home .

The organization homepage displays your organization's brand and statistics at the top of the page. The following table describes the remaining sections of the page:

Edit the Organization Branding

You can edit your organization's image and name.

Image

Follow these steps to change your organization image:

1. Open the organization homepage .
2. Click the organization image.
3. Click Choose file , select a file from your device, and then click Open .

Your organization image must be in gif , jpg , or png format and less than 1MB in size.

4. Click Save .

"Photo Uploaded" displays.

Name

Follow these steps to change your organization name:

1. Open the organization homepage .
2. Hover over the organization name and then click the pencil icon that appears.

3. Enter the new organization name and then click Save Changes .

"Organization Name Updated Successfully" displays.

View All Organizations

To view all of the organizations for which you're a member, log in to the Algorithm lab and then, in the top navigation bar, click Connected as: organizationName .

Add Organizations

Follow these steps to add new organizations to your profile:

1. Log in to the Algorithm Lab.
2. In the top navigation bar, click Connected as: organizationName .
3. In the Switch Organization panel, click Create Organization .
4. Enter the organization name and then click Add .

The organization name must be unique. "Created Successfully" displays.

Switch Organizations

Follow these steps to switch organizations:

1. Log in to the Algorithm Lab.
2. In the top navigation bar, click Connected as: organizationName .
3. In the Switch Organization panel, click the name of the organization for which you want to connect.

The top navigation bar displays the new organization name.

Set a Preferred Organization

Follow these steps to set your preferred organization:

1. Log in to the Algorithm Lab.
2. In the top navigation bar, click Connected as: organizationName .
3. In the Switch Organization panel, select the radio button under the Preferred column that corresponds to the organization that you want to set as the preferred organization.

"Preferred organization selected" displays. Refresh the page to connect as your preferred organization.

Get Organization Id

To get the organization Id, open Organization > Home and check the URL. For example, the organization Id of https://www.quantconnect.com/organization/5cad178b20a1d52567b534553413b691 is 5cad178b20a1d52567b534553413b691.

Out of Sample Period

To reduce the chance of overfitting, organization managers can enforce all backtests must end a certain number of months before the current date. For example, if you set a one year out-of-sample period, the researchers on your team will not be able to use the most recent year of data in their backtests. A out-of-sample period is helpful because it leaves you a period to test your model after your done the development stage. Follow these steps to change the backtest out-of-sample period:

1. Open the organization homepage .
2. Scroll down to the Backtesting Out of Sample Period section.
3. Adjust the out-of-sample period duration or click on "No Holdout Period".

Organizations > Billing

Introduction

The owner of an organization is responsible for the billing, but the responsibility can be delegated in Trading Firm or Institution organizations. Your organization's billing information is never saved by QuantConnect. It’s passed to the Stripe billing system. If you cancel your subscription, your live trading nodes stop running. So, for user safety, your subscriptions automatically renew each month.

View Billing Information

The Billing page displays your organization's billing details. To view the page, log into the Algorithm Lab and then, in the left navigation bar, click Organization > Billing .

The Billing page displays the billing cost, date, and frequency at the top of the page. The following table describes the remaining sections of the page:

Edit Billing Details

Follow these steps to edit your organization's billing details:

1. Open the Billing page.
2. In the Details section, click Edit .
3. Enter the new billing details and then click Save .

The Details section displays the new billing details.

Edit the Credit Card

You can add, replace, and remove the organization's credit card.

Add

Follow these steps to add a credit card:

1. Open the Billing page.
2. Click Add Card .
3. Enter the credit card details and then click Save .

The Credit Card section displays the last 4 digits of your credit card.

Replace

Follow these steps to change the credit card:

1. Open the Billing page.
2. In the Credit Card section, click Edit .
3. Enter the new credit card details and then click Save .

The Credit Card section displays the last 4 digits of your new credit card.

Remove

Follow these steps to remove the credit card:

1. Open the Billing page.
2. Click Remove .
3. Click OK .

The Credit Card section displays "No entries found".

Download Invoices and Receipts

Follow these steps to download invoices and receipts:

1. Open the Billing page.
2. Scroll down to the Invoices section and then click Download as PDF next to the invoice or receipt that you want to download.
3. Click Download invoice or Download receipt .

A prompt to download the file to your local machine displays.

Change Organization Tiers

You can change your organization to any of the paid tiers or the Free tier.

Paid Tiers

Follow these steps to change to a paid organization tier:

1. Log in to the Algorithm Lab.
2. In the left navigation bar, click Organization > Home .
3. On the organization homepage, click Edit Plan .
4. Click the Choose a Plan tab.
5. Click CHOOSE TIER under the organization tier you want.
6. Select a tier pack.

The following table describes the type of packs we have available:

Pack Type	Description
Suggested Packs	Packs with pre-selected team seats, support seats, and nodes.
Build Your Own Pack	Packs with custom selections for team seats, support seats, add-ons, and market subscriptions.

7. Select monthly or annual billing.
   
8. (Optional) Click + Add Coupon and then enter your coupon code.
   
9. If your organization doesn't have a credit card added, click Proceed to Checkout and then enter your credit card details.
10. Click Update Subscriptions or Subscribe Now .

Free Tier

Follow these steps to downgrade to the Free tier:

1. Log in to the Algorithm Lab.
2. In the left navigation bar, click Organization > Home .
3. On the organization homepage, click Downgrade to Free .
4. Click Cancel My Plan .
5. Click the I understand that my subscription will be terminated immediately check box and then click Cancel My Subscription .

Pause and Resume Subscriptions

If you're not going to utilize your QuantConnect subscriptions for a month or two, pause your subscriptions to avoid unnecessary charges. Pausing suspends access to your account, but all your cloud configuration, project files, and set up will be kept as is. When you resume your subscriptions, your account will be the same as you left it.

Pause Subscriptions

Follow these steps to pause your subscriptions:

1. Open the Billing page.
2. In the Details section, click Cancel .
3. On the Downgrade page, click Pause for 1 month or Pause for 2 months .
4. Click Pause My Subscription .

Resume Subscriptions

To resume your paused subscriptions, open the Billing page and then click Resume . If you prematurely resume your subscriptions, the credit you receive is pro-rated based on the duration of the pause period.

Payment Failures

If your payment fails, Stripe automatically tries again after 6-12 hours.

Organizations > Credit

Introduction

We created QuantConnect Credit (QCC) to enable micropayments on QuantConnect. You can use QCC to optimize parameters, download datasets, gift to members in the forum, and apply to your organization's monthly invoice. You can purchase QCC in the Algorithm Lab at a rate of 1 QCC = $0.01 USD. Since QCC is owned by organizations instead of members, all of the members within your organization have the ability to spend the QCC balance.

Optimizing Parameters

You need QCC in your organization to unlock parameter optimization . Parameter optimization jobs use optimization nodes, which are rented on a time basis. Therefore, the longer it takes to run all of the backtests in your optimization job, the more QCC it costs. Before you run optimizations, we estimate on how much QCC it will cost to run the job, but the final cost can differ from our estimates because we can't know exactly how long all of the backtests will take to run ahead of time. For instructions on optimizing parameters, see Launch Optimization Jobs .

Downloading Datasets

You can spend some QCC to download datasets from the Dataset Market to your local machine. The cost of downloading depends on the dataset and it's calculated on a per-file or per-day basis. For instructions on downloading datasets, see Licensing .

Giving to Others

To show your appreciation for contributions in the forum, give some QuantConnect Credit (QCC) rewards . The following table shows the available QCC rewards:

Award	Description	Cost (QCC)
Silver Award	A simple token of recognition from one quant to another. Keep up the great work.	80
Gold Award	This is some great work! Gold star!	600
Platinum Award	Highly resistant to oxidation, this award is for those contributions which will stand the test of time. Strong, classic, and useful for most high technology products.	1,200
Medal for Excellence	The QuantConnect Medal for Excellence is awarded by a member of the QuantConnect staff for exceptional contributions to the QuantConnect community.	3,000
Plutonium Award	Nuclear hot! This post is incredible and deserves recognition as such. Show the author your appreciation for their work.	2,000
Docs Shakespeare	You've left a mark by a contribution to the documentation for the community. Your edits and examples will be followed for generations to come.	500
Nobel Laureate	Bestowed in recognition of quantitative advances.	80
Spaghetti Code Award	Following the intricate flows of code noodle, this code compiles and runs.	300
Jedi Quant	Quant promise you show. Your code channel the force into. Hmmmmmm.	200
Live Trader	That looks like a wild ride.	50
Today I Learned	Thank you for upgrading my brain.	50
Machine Unlearning	I plan on letting the computers do all the work for me.	150
Totally Overfit	I see parameters everywhere.	80
Mind Blown Award	Something incredibly amazing, mind-boggling, and you're shocked senseless.	80
Research Rembrandt	A Jupyter notebook work of art, pulling together all the right hues and plots to be a true masterpiece.	100
Cayman Island Award	Let's get a boat and start a fund together. Did I mention the boat?	800
Printing Money Award	Awarded to profitable algorithms.	100
Stronger Together Award	We're stronger together. Let's make this happen!	120
Appreciate the Support Award	Quant trading is a hard road, we could all use a hand.	250
Master Craftsman	You're a master craftsman, taking raw materials and molding them into works of art for the good of the world.	600

Purchase QCC

Follow these steps to purchase QuantConnect Credit (QCC):

1. Open the Billing page.
2. Scroll down to the Organization Credit (QCC) section and then click Purchase Credit .
3. Select a credit pack and then click Continue .
4. If your preferred organization has a credit card and you want to charge that card, click Purchase .
5. If your preferred organization doesn't have a credit card, enter the credit card details and then click Purchase .

Only the preferred organization can be charged when purchasing QCC. If you want to charge a different organization you are a member of, set it as your preferred organization .

Apply QCC to Invoices

You can apply QCC to your organization's invoices to pay for your subscriptions, but you must enable invoice payments before your invoice is generated to pay it with QCC. To enable invoice payment with QCC, open the Billing page, scroll down to the Organization Credit (QCC) section, and then select the Only apply organization QCC to invoices check box.

Organizations > Training

Introduction

Onboard new team members to your organization through the content in the Learning Center . The Learning Center enables you to systematically track and monitor the progress of your team members on the courses you purchase or create. If you purchase or create a course, you can access it from the Organization > Resources page in the Algorithm Lab.

Paid Courses

Currently, there are only free tutorials in the Learning Center, but we expect to expand the paid course offerings through 2023. We charge for paid courses based on the number of members in your organization. For example, if there are 5 team members in your organization, multiply the listed price of a course by 5. When you purchase a course, the team members in your organization have lifetime access to the course.

Private Courses

Private courses are not for sale in the Learning Center. You can upload private courses to your Organization > Resources page, but they will not be available for other organizations to purchase. Create private courses to help onboard your team members while using the familiar Learning Center environment. Since the Learning Center has built-in tools to help you monitor your team members, you can track the progress of your team members as they work on your internal private courses.

This feature is designed for Institutional clients with large teams of quants on QuantConnect

Learning Center

Learning Center > Training

Introduction

The Learning Center is a coding environment to learn quantitative trading using LEAN. The Learning Center features a collection of courses from educators from the QuantConnect team and the community. The goal of the Learning Center is to give you an understanding of robust algorithm design and the tools you need to implement your own trading strategies. As you work through the courses, you’ll manage a portfolio, use indicators in technical trading strategies, trade on universes of assets, automate trades based on market behavior, and understand how data moves through your algorithm. After you've completed a course, you can keep the code to perform further research and deploy to live trading. Start learning today because you need to complete 30% of the Bootcamp lessons to post in the community forum.

Onboard new team members to your organization through the content in the Learning Center. The Learning Center enables you to systematically track and monitor the progress of your team members on the courses that you purchase or create. If you purchase or create a course, you can access it from the Organization > Resources page in the Algorithm Lab.

Articles

QuantConnect maintains collections of related tutorials we call a Learning Series . We have tutorial series covering the topics below - each with a set of articles or tutorials:

The Strategy Library is a collection of tutorials written by the QuantConnect team and community members. Review these tutorials to learn about trading strategies found in the academic literature and how to implement them with QuantConnect/LEAN.

Introduces basic Python functionality in the context of quantitative finance.

Introduces Options to those who are Option novices and have basic knowledge of applied mathematics, statistics, and financial markets.

Simple Options trading algorithms on QuantConnect for those who already have basic knowledge of Options markets.

Paid Courses

Currently, there are only free tutorials in the Learning Center, but we expect to expand the paid course offerings through 2023. We charge for paid courses based on the number of members in your organization. For example, if there are 5 team members in your organization, multiply the listed price of a course by 5. When you purchase a course, the team members in your organization have lifetime access to the course.

Private Courses

Private courses are not for sale in the Learning Center. You can upload private courses to your Organization > Resources page, but they will not be available for other organizations to purchase. Create private courses to help onboard your team members while using the familiar Learning Center environment. Since the Learning Center has built-in tools to help you monitor your team members, you can track the progress of your team members as they work on your internal private courses.

This feature is designed for Institutional clients with large teams of quants on QuantConnect

View All Courses

The Available Courses page displays all of the courses in the Learning Center. To view the page, open the Algorithm Lab and then, in the left navigation bar, click Learning Center > All Courses .

Each course displays the following information:

• Name
• Description
• Author
• Price
• Review summary
• The number of students

Click a course to learn more about it, including the following:

• Instructor biography
• Requirements
• Syllabus
• Reviews

Enroll in Courses

Follow these steps to enroll in Learning Center courses:

1. Log in to the Algorithm Lab.
2. In the left navigation bar, click Learning Center > All Courses .
3. On the Available Courses page, click the course in which you want to enroll.
4. Click Enroll .

The Learning Center environment displays.

Navigate the Course IDE

The course IDE automatically displays when you enroll in a course.

Follow these steps to navigate the course IDE:

1. Read the instructions in the left panel.
2. Update the main.py file with your answer.
3. (Optional) Scroll down to the bottom of the instruction panel and click Show Hint to show a hint.

A hint displays at the bottom of the instruction panel.

4. (Optional) Scroll down to the bottom of the instruction panel and click Solution to show the solution file.

A solution.py file displays.

5. (Optional) Click Reset to reset the main.py file.
6. Click Submit to check your answer.

The Chart panel displays your backtest results.

7. If an error message displays, restart from step 2.
8. Click Continue .

View Course Progress

Follow these steps to view your course progress:

1. Log in to the Algorithm Lab.
2. In the left navigation bar, click Learning Center > All Courses .
3. On the Available Courses page, click the course for which you want to see your progress.

On the course page, the About this Course section displays your progress.

View Completed Courses

Log in to the Algorithm Lab and then, in the left navigation bar, click Learning Center > Completed to view your completed courses.

Submit Reviews

You need to complete a course before you can submit a review on it.

Follow these steps to review courses:

1. Open the Completed Courses page.
2. Click the course for which you want to submit a review.
3. Scroll down to the User Reviews section and then click the number of stars you want to give the course in your review.
4. Write your review.
5. Click Submit Review .

The User Reviews section displays your review.

Report Errors

To report errors you experience with courses, email us an explanation of the error and the task URL where the error occurred.

Learning Center > Educators

Introduction

Educators are QuantConnect experts who contribute courses to the Learning Center. We are always looking for experts to become Educators and share their insight with the community. As an Educator, you create the description, requirements, and lessons of each course you contribute. You also provide the images for the course listing in the Learning Center. As students complete your course, you'll receive course reviews so you can improve your course material.

Registration

Anyone can register to become an Educator. We are looking for Educators that specialize in the following areas:

• Quant finance
• HTML, CSS, and Javascript
• C# and Python
• Jupyter

If you have the skills listed above, contact us to start the registration process.

Compensation

Educators are compensated in knowledge, commissions, and exposure. You earn a $5,000 commission per accepted course and a 70% revenue share for your courses. When your courses are listed in the Learning Center, your name and social media accounts are public, giving you exposure to the QuantConnect community.

Learning Center > Course Structure

Introduction

The courses in the Learning Center are structured in a way so you can complete courses at your own pace. The course structure enables you to improve your skills in finance, statistics, and software development while learning the QuantConnect API in easily digestible portions. The idea behind the course lessons is to focus on implementing individual strategies rather than learning just the theory.

Lessons

Courses are broken up into multiple lessons. Lessons are made up of videos, readings, and coding exercises. Lessons break up the process of learning into digestible tasks. Each lesson builds on an understanding of the API from the lesson before it, so we recommend completing the lessons in order. The introductory video of each lesson demonstrates the process of completing the tasks in the lesson before you implement them yourself.

Tasks

Lessons are broken up into multiple tasks to test your understanding of the course topic. Each task is accompanied by text instruction to guide you to complete the task. The Learning Center environment, where you complete each task, is similar to the regular web IDE used for backtesting and live trading. After you read the task instructions and run your solution algorithm, you are informed if you completed the task. If you need assistance, you can view a hint or the full solution file .

Results

To check if you have completed a task correctly, backtest your algorithm in the Learning Center environment and then the result window displays your results. If you receive an error message, update the code and then run the backtest again. If you pass the task, you'll be prompted to proceed to the next task in the course. If you're having trouble completing the task, you can copy the task solution file.

Solutions

A solution file for each task is available in the Learning Center environment. You may use it, but we recommend you try to solve the problem before you check the solution file. You'll learn best by solving the problem on your own. However, errors can occur when running backtests, so you may use the solution file to ensure the environment is running without error.

Errors

If you run into an error when you are working on a task, compare your code to the solution file. The error may be caused by either an error in your submission or an error in the backend of the Learning Center environment. In cases when errors occur in the backtest of the Learning Center environment, email us the following information:

• The URL of the task.
• An explanation of the issue.
• A code snippet to reproduce the error.

Projects

Projects > Getting Started

Introduction

Projects contain files to run backtests, launch research notebooks, perform parameter optimizations, and deploy live trading strategies. You need to create projects in order to create strategies and share your work with other members. Projects enable you to generate institutional-grade reports on the performance of your backtests. You can create your projects from scratch or you can utilize pre-built libraries and third-party packages to expedite your development process.

The Algorithm lab enables you to create, store, and manage your projects in the cloud. You can only access your own projects unless you share them with others, or add collaborators.

View All Projects

The All Projects page displays all of your QuantConnect projects in the organization, including libraries and Boot Camp lessons.

Click a project or directory on the page to open it.

Follow these steps to view the page:

1. Log in to the Algorithm Lab.
2. In the left navigation bar, click Projects .
3. Click Open Project .

Create Projects

Follow these steps to create new projects:

1. Log in to the Algorithm Lab.
2. In the left navigation bar, click Projects .
3. On the Projects page, click Create New Algorithm .

The web IDE displays an empty project.

Close Projects

In the Project panel, click Close to close projects.

Clone Projects

Clone a project to create a new copy of the project and save it within the same organization. When you clone a project, the project files are duplicated but the backtest results and live deployment history are not retained. Cloning enables you to test small changes in your projects before merging the changes back into the original project and start a new live deployment record.

To clone a project, open the project and then, in the Project panel, click Clone .

Migrate Projects

Migrating moves a project from one organization to another. You must be the organization administrator to migrate projects out of the organization. Migrate a project to run the project using resources from a different organization and to collaborate on the project with members from a different organization. When you migrate projects, the project files are copied but the content stored in the Object Store is not retained.

To migrate a project, open the project and then, in the Project panel, click Migrate .

Rename Projects

Follow these steps to rename a project:

1. Open the project .
2. In the Project panel, hover over the project name and then click the pencil icon that appears.

3. In the Name field, enter the new project name and then click Save Changes .

The project name must only contain - , _ , letters, numbers, and spaces. The project name can't start with a space or be any of the following reserved names: CON, PRN, AUX, NUL, COM1, COM2, COM3, COM4, COM5, COM6, COM7, COM8, COM9, LPT1, LPT2, LPT3, LPT4, LPT5, LPT6, LPT7, LPT8, or LPT9.

Create Project Directories

Set the name of a project to directoryName / projectName to create a project directory.

Set Descriptions

Follow these steps to set the project description:

1. Open the project .
2. In the Project panel, hover over the project name and then click the pencil icon that appears.

3. In the Description field, enter the new project description and then click Save Changes .

Edit Parameters

Algorithm parameters are hard-coded values for variables in your project that are set outside of the code files. Add parameters to your projects to remove hard-coded values from your code files and to perform parameter optimizations. You can add parameters, set default parameter values, and remove parameters from your projects.

Add Parameters

Follow these steps to add an algorithm parameter to a project:

1. Open the project .
2. In the Project panel, click Add New Parameter .
3. Enter the parameter name.

The parameter name must be unique in the project.

4. Enter the default value.
5. Click Create Parameter .

To get the parameter values into your algorithm, see Get Parameters .

Set Default Parameter Values

Follow these steps to set the default value of an algorithm parameter in a project:

1. Open the project .
2. In the Project panel, hover over the algorithm parameter and then click the pencil icon that appears.

3. Enter a default value for the parameter and then click Save .

The Project panel displays the default parameter value next to the parameter name.

Delete Parameters

Follow these steps to delete an algorithm parameter in a project:

1. Open the project .
2. In the Project panel, hover over the algorithm parameter and then click the trash can icon that appears.

3. Remove the GetParameter calls that were associated with the parameter from your code files.

Delete Projects

You can delete a project when it is open or closed.

Delete Open Projects

In the Project panel, click Delete , and then click Yes to delete the project.

Delete Closed Projects

Follow these steps to delete the project:

1. Open the My Projects page.
2. If the project is in a directory, click the directory files to navigate to the project file.
3. Hover over the project file and then click the trash can icon that appears.


"Project deleted" displays.

Encrypt Projects

When you save projects in QuantConnect Cloud, you can save encrypted versions of your project files instead of the raw, human readable, file content. Encrypting your projects gives you an additional layer of protection. To use the encryption system, you provide your own encryption key, which your local browser saves to memory. For more information about project encryption, see Encryption .

Get Project Id

To get the project Id, open the project and check the URL. For example, the project Id of https://www.quantconnect.com/project/13946911 is 13946911.

Projects > Structure

Introduction

Projects organize your algorithm data. They have settings, files, results, and attached libraries.

Your account has a directory to organize the projects that you have access to in each of your organizations. If you switch the organization that you are connected as , your directory of projects is updated to reflect the projects that you have access to within the new organization.

Files

New projects contain code files ( .py or .cs ) and notebook files ( .ipynb ). Run backtests with code files and launch the Research Environment with notebook files. Code files must stay within your size quotas . To keep files small, files can import code from other code files. To aid navigation, you can rename, move, and delete files in the web IDE. Notebook files save the input cells, but not the output cells.

Directories

Your directory of projects can contain nested directories of projects to make navigation easier. Similarly, the code and notebook files in your projects can contain nested directories of files. For example, if you have multiple Alpha models in your strategy, you can create an alphas directory in your project to hold a file for each Alpha model.

The following directory names are reserved: .ipynb_checkpoints , .idea , .vscode , __pycache__ , bin , obj , backtests , live , optimizations , storage , and report .

Description

You can give a project a description to provide a high-level overview of the project and its functionality. Descriptions make it easier to return to old projects and understand what is going on at a high level without having to look at the code. The project description is also displayed at the top of backtest reports , which you can create after your backtest completes.

Libraries

Libraries are reusable code files that you can import into any project for use in backtesting, research, and live trading. Use libraries to increase your development speed and save yourself from copy-pasting between projects. You can create libraries and add them to your projects using the web IDE. Your libraries are saved under the Library directory in the Algorithm Lab.

Parameters

Algorithm parameters are hard-coded values for variables in your project that are set outside of the code files. Add parameters to your projects to remove hard-coded values from your code files and to perform parameter optimizations . To get the parameter values into your algorithm, see Get Parameters . The parameter values are sent to your algorithm when you deploy the algorithm, so it's not possible to change the parameter values while the algorithm runs.

Projects > Files

Introduction

The files in your projects enable you to implement trading algorithms, perform research, and store important information. Python projects start with a main.py and a research.ipynb file. C# projects start with a Main.cs and a Research.ipynb file. Use the main.py or Main.cs file to implement trading algorithms and use the ipynb file to access the Research Environment.

Supported File Types

The IDE supports the following file types:

• .cs
• .ipynb
• .py
• .html
• .css

Add Files

Follow these steps to add a file to a project:

1. Open the project .
2. In the right navigation menu, click the Explorer icon.
3. In the Explorer panel, expand the Workspace (Workspace) section.
4. Click the New File icon.
5. Enter a file name and extension.
6. Press Enter .

Add Directories

Follow these steps to add a directory to a project:

1. Open the project .
2. In the right navigation menu, click the Explorer icon.
3. In the Explorer panel, expand the Workspace (Workspace) section.
4. Click the New Directory icon.
5. Enter a directory name and then press Enter .

The following directory names are reserved: .ipynb_checkpoints , .idea , .vscode , __pycache__ , bin , obj , backtests , live , optimizations , storage , and report .

Open Files

Follow these steps to open a file in a project:

1. Open the project .
2. In the right navigation menu, click the Explorer icon.
3. In the Explorer panel, click the file you want to open.

Close Files

To close a file, at the top of the IDE, click the x button on the file tab you want to close.

To close all of the files in a project, at the top of the IDE, right-click one of the file tabs and then click Close All .

Rename Files and Directories

Follow these steps to rename a file or directory in a project:

1. Open the project .
2. In the right navigation menu, click the Explorer icon.
3. In the Explorer panel, right-click the file or directory you want to rename and then click Rename .
4. Enter the new name and then press Enter .

The following directory names are reserved: .ipynb_checkpoints , .idea , .vscode , __pycache__ , bin , obj , backtests , live , optimizations , storage , and report .

Delete Files and Directories

Follow these steps to delete a file or directory in a project:

1. Open the project .
2. In the right navigation menu, click the Explorer icon.
3. In the Explorer panel, right-click the file or directory you want to delete and then click Delete Permanently .
4. Click Delete .

Size Quotas

The maximum file size you can have in a project depends on your organization's tier. The following table shows the quota of each tier:

Projects > IDE

Introduction

The web Integrated Development Environment (IDE) lets you work on research notebooks and develop algorithms for backtesting and live trading. When you open a project , the IDE automatically displays. You can access your trading algorithms from anywhere in the world with just an internet connection and a browser. If you prefer to use a different IDE, the CLI allows you to develop locally in your preferred IDE.

Supported Languages

The Lean engine supports C# and Python. Python is less verbose, has more third-party libraries, and is more popular among the QuantConnect community than C#. C# is faster than Python and it's easier to contribute to Lean if you have features written in C# modules. Python is also the native language for the research notebooks, so it's easier to use in the Research Environment.

The programming language that you have set on your account determines how autocomplete and IntelliSense are verified and determines the types of files that are included in your new projects. If you have Python set as your programming language, new projects will have .py files. If you have C# set as your programming language, new projects will have .cs files.

Change Languages

Follow these steps to select a programming language:

1. Log in to the Algorithm Lab.
2. In the top navigation bar, click yourUsername > My Account .
3. On your Account page, in the Account Settings section, click C# or Python .

"Preferred language setting has been updated" displays.

Autocomplete and Intellisense

Intellisense is a GUI tool in your code files that shows auto-completion options and presents the members that are accessible from the current object. The tool works by searching for the statement that you're typing, given the context. You can use Intellisense to auto-complete method names and object attributes. When you use it, a pop-up displays in the IDE with the following information:

• Member type
• Member description
• The parameters that the method accepts (if the member is a method)

Use Intellisense to speed up your algorithm development. It works with all of the default class members in Lean, but it doesn't currently support class names or user-defined objects.

Use Autocomplete

Follow these steps to use autocomplete:

1. Open a project .
2. Type the first few characters of a variable, function, class, or class member that you want to autocomplete (for example, self.set Set or SimpleMovingAverage.Upda ).
3. Press CTRL+Space .

If there are class members that match the characters you provided, a list of class members displays.


4. Select the class member that you want to autocomplete.

The rest of the class member name is automatically written in the code file.

Console

The console panel at the bottom of the IDE provides some helpful information while you're developing algorithms.

Cloud Terminal

The Cloud Terminal tab of the panel shows the API messages, errors, and the logs from your algorithms.

To clear the Cloud Terminal, click the Clear Logs icon in the top-right corner of the panel.

Ask Mia

The Ask Mia tab of the panel is where you can interact with our AI assistant, Mia.

Mia provides contextual assistance to most issues you may encounter when developing a strategy, including build errors, API methods, and best coding practices. It has been trained on hundreds of algorithms and thousands of documentation pages.

To clear the chat with Mia, click the Clear Mia Chat icon in the top-right corner of the panel.

Problems

The Problems tab of the panel highlights the coding errors in your algorithms.

Manage Nodes

The Resources panel shows the cloud backtesting, research, and live trading nodes within your organization.

To view the Resources panel, open a project and then, in the right navigation menu, click the Resources icon.

The panel displays the following information for each node:

To stop a running node, click the stop button next to it. You can stop nodes that you are using, but you need stop node permissions to stop nodes other members are using.

By default, we select the best node available in your clusters when you launch a backtest or research notebook. To use a specific node, click the check box next to a node in the panel.

Navigate the File Outline

The Outline section in the Explorer panel is an easy way to navigate your files. The section shows the name of classes, members, and functions defined throughout the file. Click one of the names to jump your cursor to the respective definition in the file. To view the Outline , open a project and then, in the right navigation menu, click the Explorer icon.

Split the Editor

The editor can split horizontally and vertically to display multiple files at once. Follow these steps to split the editor:

1. Open a project .
2. In the right navigation bar, click the Explorer icon.
3. In the QC (Workspace) section, drag and drop the files you want to open.

Use this feature instead of opening multiple browser tabs for a single project. If you open open multiple browser tabs, two code sessions will be updating the same project, which will cause the code sessions to fall out of sync.

Show and Hide Code Blocks

The editor can hide and show code blocks to make navigating files easier. To hide and show code blocks, open a project and then click the arrow icon next to a line number.

Keyboard Shortcuts

Keyboard shortcuts are combinations of keys that you can issue to manipulate the IDE. They can speed up your workflow because they remove the need for you to reach for your mouse.

Follow these steps to view the keyboard shortcuts of your account:

1. Open a project .
2. Press F1 .
3. Enter "Preferences: Open Keyboard Shortcuts".
4. Click Preferences: Open Keyboard Shortcuts .

To set a key binding for a command, click the pencil icon in the left column of the keyboard shortcuts table, enter the key combination, and then press Enter .

Themes

The Algorithm Lab offers light and dark themes. Follow these steps to change themes:

1. Log in to your account.
2. In the top navigation bar, click yourUsername > My Account .
3. On your Account page, in the Account Settings section, click Light Theme or Dark Theme

Your Account page refreshes and displays the new theme.

Supported Browsers

The IDE works with Chrome, Edge, Firefox, and Safari. For more information about browser support, see Browser Support in the Visual Studio Code documentation.

Cookies

Cookies are essential for the Algorithm Lab to connect to the VS Code environment because modern browsers treat the coding environment domain (e.g., {...}.code.qc.com) as a third-party domain relative to the main domain (www.qc.com). This distinction means that if cookies for the coding environment are blocked, essential functions like user authentication, passing messages, adjusting panel sizes, setting tasks in bootcamp, and encrypting/decrypting project files will not work correctly, leading to a disrupted user experience. Enabling cookies ensures that your coding session remains seamless and fully functional. To enable third-party cookies, see the support page of the following browsers:

• Chrome
• Edge
• Firefox
• Safari

If you use Safari, disable "Prevent cross-site tracking" .

If you use Chrome, add [*.]quantconnect.com under "Sites allowed to use third-party cookies".

Troubleshooting

If you experience issues trying to load the IDE, follow these steps:

1. Check if you're using one of the supported browsers .
2. Check your internet connection and speed.
3. Test a different supported browser.
4. Enable cookies .
5. Disable your Virtual Private Network (VPN).
6. Disable your browser add-ons.
7. Check your anti-virus settings.

The internet protection of some anti-virus products block the "Service Workers" that the IDE needs to operate. Kaspersky and Avast are the two products that commonly block the IDE from using Service Workers. These are background threads that improve the IDE's experience.

8. Configure your network settings to use Google Public DNS .

The DNS settings of some ISPs block Microsoft DNS, so some panels don't load and display the following message: "Server IP address could not be found".

The following resources explain how to change your networks settings:

• Firefox DNS-over-HTTPS
• DNS Encryption with DNS over HTTPs (DoH) on Chrome
• Microsoft Edge to have DNS over HTTPS (DoH) as the default DNS settings
• Set up 1.1.1.1 - macOS
9. Try to load the IDE with a different computer, tablet, or cell phone.
10. Clear your browser cache, especially if you created your project before the new IDE (March 2022).

Your browser may cache data from the old IDE.

11. Check if your hard drive is full.
12. Restart your computer and internet router.

If these steps don't solve the issues, send the logs to the Console Inspector to the Support Team .

Projects > Encryption

Introduction

When you save projects in QuantConnect Cloud, you can save encrypted versions of your project files instead of the raw, human readable, file content. To use the encryption system, you provide your own encryption key, which your local browser saves to memory. Afterwards, whenever you save your project, your browser uses the local key to encrypt your project files and then only the obfuscated files are pushed to QuantConnect Cloud.

View All Keys

The organization homepage displays the name of all your organization's encryption keys and provides a way to upload more. A MD5 hash of the key is recorded to know which key encoded a project.

The icon means the key is registered in the database but we could not locate it on your computer. This may happen when you login from a new computer. To use this key, click Use Key and upload the key with the pop-up. This process stores a copy of your key in the browser local store so you can decrypt the relevant projects in your organization.

Add Keys

Follow these steps to add an encryption key to your local storage:

1. Navigate to the Encryption Keys section of your organization homepage.
2. Click Add Encryption Key .
3. In the Add Encryption Key window, enter a name for the key and then add the encryption key file.

The encryption key file must be a txt file with at least 32 characters. It’s content can be arbitrary.

4. Click Add Key .

Delete Keys

Follow these steps to delete an encryption key:

1. Navigate to the Encryption Keys section of your organization homepage.
2. Click Delete next to the key you want to delete.
3. Click Delete .

Add Encryption

Follow these steps to add encryption to your project files:

1. Open a project or create a new one .
2. In the Project panel, click Encrypt Project .
3. If you have added an encryption key before and want to use it for this project, click the key name from the drop-down menu.
4. If you haven’t added an encryption key before or you want to add a new one, follow these steps:
1. Click Add Key to Organization from the drop-down menu.
2. In the Add Encryption Key window, enter a name for the key and then add the encryption key file.

The encryption key file must be a txt file with at least 32 characters. It’s content can be arbitrary.

3. Click Add Key .
5. Click Encrypt .

Remove Encryption

To remove encryption from a project, open the project and then, in the Project panel, click the unlock icon next to the name of your encryption key.

Collaboration Support

Encryption isn’t available for projects that have collaborators .

Libraries

Encrypted projects can use libraries encrypted with the same project key or unencrypted libraries. However, you cannot use a library encrypted with a different project encryption key.

To encrypt a library, open its project and set its encryption key .

Projects > Debugging

Introduction

Debugging is the process of systematically using a tool to find and fix errors in software. Errors can cause unintended trades, unexpected algorithm crashes, and faulty risk management logic. We use a debugging tool step through code line-by-line, and inspect the variables to understand the internal state of the program. You have many tools to debug your algorithm, including our built-in debugger, logging statements, charting, and the Object Store.

Coding Errors

Coding errors are errors that cause your projects to not build, throw exceptions, or behave unexpectedly. There are generally 3 types of coding errors: build, runtime, and logic errors. Each type of error occurs for different reasons.

Build Errors

Build errors occur when the interpreter's syntax check fails. An example code snippet that produces a build error is the following:

var a = 1;
if (a = 2) {}

a = 1
if a = 2:
    pass

If build errors occur in your project, you can not use the debugger, logging statements, or custom charts to debug the issue. You're notified of build errors in the following ways:

• The line where the error occurs is underlined in red.
• The Problems panel at the bottom of the IDE displays the errors.
• The Explorer panel highlights the editors, files, and outlines in red where the error occurs.

Runtime Errors

Runtime Errors, also called exceptions, occur when the interpreter’s syntax checks pass but an error occurs during execution. An example code snippet that produces a runtime error is the following:

var a = new List<int>() {1};
var b = a[1];

a = [1]
a[1]

If runtime errors occur in your project, a stack trace of the error is added to the Cloud Terminal and the log file. For example, the snippet above produces the following error message:

The stack trace from the build error identifies the line of code where the error occurs. If the error doesn't reference your project files, it's an issue with Lean or another library. To view more information about the error, click (Open Stack Trace) .

Logic Errors

Logic errors occur when your algorithm behaves in an unexpected or unintended manner. These types of errors don't halt the program execution, so they are difficult to diagnose. An example code snippet that produces a logic error is the following:

var average = x + y / 2.0; // instead of (x + y) / 2.0

average = x + y / 2 # instead of (x + y) / 2

To resolve logic errors, carefully trace your algorithm. You may use the Log log method and Debug debug method methods or the built-in debugger.

Debugger

The debugger is a built-in tool to help you debug coding errors while backtesting. The debugger enables you to slow down the code execution, step through the program line-by-line, and inspect the variables to understand the internal state of the program. For more information about the backtesting debugger, see Backtest Debugging .

Logging Statements

Algorithms can record string messages ('log statements') to a file for analysis after a backtest is complete, or as a live algorithm is running. These records can assist in debugging logical flow errors in the project code. Consider adding them in the code block of an if statement to signify an error has been caught.

It's good practice to add logging statements to live algorithms so you can understand its behavior and keep records to compare against backtest results. If you don't add logging statements to a live algorithm and the algorithm doesn't trade as you expect, it's difficult to evaluate the underlying problem.

Log

Log statements are added to the log file while your algorithm continues executing. Logging dataset information is not permitted. Use Log log method statements to debug your backtests and live trading algorithms.

Log length is capped by organization tier . If your organization hits the daily limit, contact us .

If you log the same content multiple times, only the first instance is added to the log file. To bypass this rate-limit, add a timestamp to your log messages.

For live trading, the log files of each cloud project can store up to 100,000 lines for up to one year. If you log more than 100,000 lines or some lines become older than one year, we remove the oldest lines in the files so your project stays within the quota.

To record the algorithm state when the algorithm stops executing, add log statements to the OnEndOfAlgorithm on_end_of_algorithm event handler.

Log("My log message");

self.log("My log message")

Debug

Debug statements are the same as log statements, but Debug debug method statements are orange in the Cloud Terminal. Use these statements when you want to give more attention to a message in the Cloud Terminal. Debug messages can be up to 200 characters in length. If you send multiple debug statements within 1 second, your messages are rate-limited to avoid crashing your browser.

Debug("My debug message");

self.debug("My debug message")

Error

Error statements are the same as log statements, but Error error method statements are displayed in red text in the Cloud Terminal. Use these statements when you want to give the most attention to a message in the Cloud Terminal. Error statements are rate-limited like debug statements.

Error("My error message");

self.error("My error message")

Quit

Quit statements cause your project to stop running and may log some data to the log file and Cloud Terminal. These statements are orange in the Cloud Terminal. When you call the Quit quit method method, the program continues executing until the end of the method definition. If you want to quit execution immediately, return after you call Quit quit method.

Quit("My quit message");

self.quit("My quit message")

Charting

You can use the IDE charting capabilities to plot values over time when debugging. To add data points to a custom chart, call the Plot plot method with a chart name, series name, and value. For a full example, see Charting .

// Add data points to a custom chart.
Plot("Chart Name", "Series Name", value);

# Add data points to a custom chart.
self.plot("Chart Name", "Series Name", value)

If you run your algorithm in QuantConnect Cloud, we limit the number of points a chart can have to 4,000 because intensive charting generates hundreds of megabytes (200MB) of data, which is too much to stream online or display in a web browser. If you exceed the limit, the following error message is thrown:

Object Store

The Object Store is a key-value data store for low-latency information storage and retrieval. During a backtest, you can build large objects you’d like to analyze and write them for later analysis. This workflow can be helpful when the objects are large and plotting is impossible or when you want to perform analysis across many backtests.

// Save the key-value pair in the Object Store.
ObjectStore.Save("key", value);

# Save the key-value pair in the Object Store.
self.object_store.save("key", value)

For more information about the Object Store, see Object Store . For a specific example of saving indicator values during a backtest into the Object Store and then plotting them in the Research Environment, see Example for Plotting .

Projects > Collaboration

Introduction

Project collaboration is a real-time coding experience with other members of your team. Collaborating can speed up your development time. By working with other members in an organization, members within the organization can specialize in different parts of the project.

Video Demo

When there are multiple people working on the same project, the cursor of each member is visible in the IDE and all file changes occur in real-time for everyone. The following video demonstrates the collaboration feature:

Add Team Members

You need to own the project to add team members to it.

Follow these steps to add team members to a project:

1. Open the project .
2. In the Collaborate section of the Project panel, click Add Collaborator .
3. Click the Select User... field and then click a member from the drop-down menu.
4. If you want to give the member control of the project's live deployments , select the Live Control check box.
5. Click Add User .

The member you add receives an email with a link to the project.

If the project has a shared library , the collaborator can access the project, but not the library. To grant them access to the library, add them as a collaborator to the library project.

Collaborator Quotas

The number of members you can add to a project depends on your organization's tier . The following table shows the number of collaborators each tier can have per project:

Toggle Live Control

You need to have added a member to the project to toggle their live control of the project.

Follow these steps to enable and disable live control for a team member:

1. Open the project .
2. In the Collaborate section of the Project panel, click the profile image of the team member.
3. Click the Live Control check box.
4. Click Save Changes .

Remove Team Members

Follow these steps to remove a team member from a project you own:

1. Open the project .
2. In the Collaborate section of the Project panel, click the profile image of the team member.
3. Click Remove User .

To remove yourself as a collaborator from a project you don't own, delete the project .

Intellectual Property

All individuals on QuantConnect own their intellectual property (IP) on our platform. Your code is private and only accessible by people you share the project with and with support-engineers when you submit a support ticket. At no point does QuantConnect ever claim ownership of user IP. The only case where algorithm code becomes public domain is when they are shared to the forum. In this case, algorithms need to be made public domain to allow the sharing of the algorithm code.

It is common when companies hire engineers to write software, they require their employees to sign an agreement that gives the company IP ownership of any code written for work. They need this because they're paying you to write software, and the company needs to sell that software to turn a profit. Similarly, the Organizations feature allows you to control who holds IP ownership over a project. Each type of organization has its own mechanisms for handling project IP ownership.

Individual Organizations

The Free and the Quant Researcher tiers only allow single-member organizations. This means you can't collaborate with anyone else inside the QuantConnect platform. Simply put, you own the IP for any projects you work on since you are the sole collaborator.

Team Organizations

For organizations that allow multiple users to collaborate on projects, the user who created the project owns it; this can be you or one of your teammates. If you add a teammate/collaborate, they can clone it, but the original project belongs to the person who first created it.

Trading Firm & Institution Organizations

For Trading Firm and Institution organizations, which are generally used by companies and funds, the firm owns all employee projects. This is made to suit firms that wish to hire consultants and need to ensure the code remains with the company when the consultant work is finished. You have to explicitly create a project in an organization for it to be created on the organization's account.

Other Collaboration Methods

Additional methods of collaboration include cloning, sharing, and migrating projects.

Clone Projects

Clone a project to create a new copy of the project and save it within the same organization. When you clone a project, the project files are duplicated but the backtest results and live deployment history are not retained. Cloning enables you to test small changes in your projects before merging the changes back into the original project and start a new live deployment record.

To clone projects, open the project you want to clone and then, in the Project panel, click Clone . "Project cloned successfully" displays.

Share Projects

Run a backtest and then make the backtest results public to share a project. Once a backtest is made public, a link is generated for you that opens the backtest results and the project files. You can directly give the link to others, attach the backtest to a forum discussion, or embed the backtest into a website. However, note that when you make a backtest public, the project files are accessible to anyone who visits the link, even after you delete the project. As a result, we don't recommend collaborating on projects by making backtests public and sharing the link with your collaborators. Instead, add team members to your project since it protects your intellectual property.

You can share a backtest at any time when it's executing. Although, if you generate a link to share the backtest before the backtest completes, the link that's generated will not contain all of the backtest results. Some reasons to share your project include the following:

• Attach the project to the forum to ask for help, gather feedback, or report an issue.
• Attach the project to a data issue to reduce the amount of time it takes to fix the data issue.
• Share a link to the project with others to give them a copy of the project files and the backtest results.

To share a research notebook, save the notebook and run a backtest.

Migrate Projects

Migrating moves a project from one organization to another. You must be the organization administrator to migrate projects out of the organization. Migrate a project to run the project using resources from a different organization and to collaborate on the project with members from a different organization. When you migrate projects, the project files are copied but the content stored in the Object Store is not retained.

Follow these steps to migrate projects:

1. Open the project you want to migrate.
2. In the Project panel, click Migrate .
3. Click the name of the organization to which you want to migrate the project and then click Migrate .

The top navigation bar displays Connected as: theOrganizationYouMigratedTo .

Projects > Code Sessions

Introduction

Code sessions let you access a cloud hosted IDE to research and develop trading algorithms. When you open a project, a new code session starts running with the latest master branch of the LEAN trading engine. The session is ready to go with access to the full QuantConnect data library and the cloud resources of the QuantConnect technology stack.

View Code Sessions

The Projects page displays all of your running code sessions in your current organization. To view the page, log in to the Algorithm Lab and then, in the left navigation menu, click Projects .

To open one of the code sessions, click the project name.

To stop the code sessions, click the stop icon next to a project name. If you log out, the code sessions don't automatically stop.

The left navigation bar of the Algorithm Lab also shows the running code sessions underneath Projects . The blue code session represents the session that's currently open. The gray code sessions represent the sessions that are currently minimized.

Code Session Quotas

If you have a project open, it uses a coding session. Paid organizations can have multiple active coding sessions, but free users can only have one coding session open at a time. The following table shows how many active coding sessions you can have on each organization tier:

If the organization you're in has more live trading nodes than your initial coding session quota, then your coding session quota increases to the number of live trading nodes you have in the organization so you can view all your live strategies.

The quota for free organizations is a global quota, so you can have one active coding session across all of your free organizations. The quotas for paid organizations are at the organization level. Therefore, if you are in two Quant Researcher organizations, you can have two active coding sessions in one of those organizations and another two active sessions in the other organization. These paid tier quotas are for each account, not for the organization as a whole. For instance, a Trading Firm organization can have more than eight members and all of the members can simultaneously work on projects within the organization.

Projects > Shared Libraries

Introduction

Project libraries are QuantConnect projects you can merge into your project to avoid duplicating code files. If you have tools that you use across several projects, create a library.

Create Libraries

Follow these steps to create a library:

1. Create a new project .
2. In the project panel, click Add Library .
3. Click Create New .
4. In the Input Library Name field, enter a name for the library (for example, Calculators ).

To create a library directory, set the name to directoryName / libraryName (for example, Tools / Calculators ).

5. Click Create Library .

The template library files are added to your project. View the files in the Explorer panel.

6. In the right navigation menu, click the Explorer icon.
7. In Explorer panel, open the Library.py Library.cs file, rename it to reflect its purpose (e.g.: TaxesCalculator.py TaxesCalculator.cs ), and implement your library.

Add Libraries

Follow these steps to add a library to your project:

1. Open the project .
2. In the Project panel, click Add Library .
3. Click the Choose a library... field and then click a library from the drop-down menu.
4. Click Add Library (e.g. Calculators ).

The library files are added to your project. To view the files, in the right navigation menu, click the Explorer icon.

5. Import the library into your project to use the library.

using Calculators;
namespace QuantConnect.Algorithm.CSharp
{
    public class AddLibraryAlgorithm : QCAlgorithm
    {
        private TaxesCalculator _taxesCalculator = new();
    }
}

from Calculators.TaxesCalculator import TaxesCalculator
class AddLibraryAlgorithm(QCAlgorithm):
    taxes_calculator = TaxesCalculator()

Rename Libraries

To rename a library, open the library project file and then rename the project .

Remove Libraries

Follow these steps to remove a library from your project:

1. Open the project that contains the library you want to remove.
2. In the Project panel, hover over the library name and then click the trash can icon that appears.


The library files are removed from your project.

Delete Libraries

To delete a library, delete the library project file .

Projects > Package Environments

Introduction

Libraries (or packages) are third-party software that you can use in your projects. You can use many of the available open-source libraries to complement the classes and methods that you create. Libraries reduce your development time because it's faster to use a pre-built, open-source library than to write the functionality. Libraries can be used in backtesting, research, and live trading. The environments support various libraries for machine learning, plotting, and data processing. As members often request new libraries, we frequently add new libraries to the underlying docker image that runs the Lean engine.

This feature is primarily for Python algorithms as not all Python libraries are compatible with each other. We've bundled together different sets of libraries into distinct environments. To use the libraries of an environment, set the environment in your project and add the relevant using import statement of a library at the top of your file.

Set Environment

Follow these steps to set the library environment:

1. Open a project .
2. In the Project panel, click the Python Foundation field and then select an environment from the drop-down menu.

Default Environment

The default environment supports the following libraries:

absl-py                                 2.1.0
accelerate                              0.34.2
adagio                                  0.2.6
aesara                                  2.9.4
aiohappyeyeballs                        2.4.4
aiohttp                                 3.11.10
aiosignal                               1.3.1
aiosqlite                               0.20.0
alembic                                 1.14.0
alibi-detect                            0.12.0
alphalens-reloaded                      0.4.5
altair                                  5.5.0
anaconda-anon-usage                     0.4.4
annotated-types                         0.7.0
anyio                                   4.7.0
aplr                                    10.8.0
appdirs                                 1.4.4
apricot-select                          0.6.1
arch                                    7.2.0
archspec                                0.2.3
argon2-cffi                             23.1.0
argon2-cffi-bindings                    21.2.0
arrow                                   1.3.0
arviz                                   0.20.0
astropy                                 7.0.0
astropy-iers-data                       0.2024.12.9.0.36.21
asttokens                               3.0.0
astunparse                              1.6.3
async-lru                               2.0.4
attrs                                   24.2.0
Authlib                                 1.3.2
autograd                                1.7.0
autograd-gamma                          0.5.0
autokeras                               2.0.0
autoray                                 0.7.0
ax-platform                             0.4.3
babel                                   2.16.0
bayesian-optimization                   2.0.0
beautifulsoup4                          4.12.3
bleach                                  6.2.0
blinker                                 1.9.0
blis                                    0.7.11
blosc2                                  2.7.1
bokeh                                   3.6.2
boltons                                 23.0.0
botorch                                 0.12.0
Bottleneck                              1.4.2
Brotli                                  1.0.9
cachetools                              5.5.0
captum                                  0.7.0
catalogue                               2.0.10
catboost                                1.2.7
category-encoders                       2.6.4
causal-conv1d                           1.5.0.post8
certifi                                 2024.8.30
cesium                                  0.12.1
cffi                                    1.17.1
chardet                                 5.2.0
charset-normalizer                      3.3.2
check-shapes                            1.1.1
chronos-forecasting                     1.4.1
clarabel                                0.9.0
click                                   8.1.7
clikit                                  0.6.2
cloudpathlib                            0.20.0
cloudpickle                             3.1.0
cmdstanpy                               1.2.4
colorama                                0.4.6
colorcet                                3.1.0
colorlog                                6.9.0
colorlover                              0.3.0
colour                                  0.1.5
comm                                    0.2.2
conda                                   24.9.2
conda-content-trust                     0.2.0
conda-libmamba-solver                   24.9.0
conda-package-handling                  2.3.0
conda_package_streaming                 0.10.0
confection                              0.1.5
cons                                    0.4.6
contourpy                               1.3.1
control                                 0.10.1
copulae                                 0.7.9
copulas                                 0.12.0
coreforecast                            0.0.15
cramjam                                 2.9.0
crashtest                               0.3.1
creme                                   0.6.1
cryptography                            43.0.0
cucim-cu12                              24.8.0
cuda-python                             12.6.2.post1
cudf-cu12                               24.8.3
cufflinks                               0.17.3
cugraph-cu12                            24.8.0
cuml-cu12                               24.8.0
cuproj-cu12                             24.8.0
cupy-cuda12x                            13.3.0
cuspatial-cu12                          24.8.0
cuvs-cu12                               24.8.0
cuxfilter-cu12                          24.8.0
cvxopt                                  1.3.2
cvxportfolio                            1.4.0
cvxpy                                   1.6.0
cycler                                  0.12.1
cymem                                   2.0.10
Cython                                  3.0.11
darts                                   0.31.0
dash                                    2.9.3
dash-core-components                    2.0.0
dash_cytoscape                          1.0.2
dash-html-components                    2.0.0
dash-table                              5.0.0
dask                                    2024.7.1
dask-cuda                               24.8.2
dask-cudf-cu12                          24.8.3
dask-expr                               1.1.9
databricks-sdk                          0.38.0
dataclasses-json                        0.6.7
datasets                                2.21.0
datashader                              0.16.3
deap                                    1.4.1
debugpy                                 1.8.9
decorator                               5.1.1
deepmerge                               2.0
defusedxml                              0.7.1
Deprecated                              1.2.15
deprecation                             2.1.0
dgl                                     2.1.0
dill                                    0.3.8
dimod                                   0.12.17
dirtyjson                               1.0.8
diskcache                               5.6.3
distributed                             2024.7.1
distributed-ucxx-cu12                   0.39.1
distro                                  1.9.0
dm-tree                                 0.1.8
docker                                  7.1.0
docutils                                0.21.2
DoubleML                                0.9.0
dropstackframe                          0.1.1
dtreeviz                                2.2.2
dtw-python                              1.5.3
dwave-cloud-client                      0.13.1
dwave-drivers                           0.4.4
dwave-gate                              0.3.2
dwave-greedy                            0.3.0
dwave-hybrid                            0.6.12
dwave-inspector                         0.5.1
dwave-inspectorapp                      0.3.3
dwave-neal                              0.6.0
dwave_networkx                          0.8.15
dwave-ocean-sdk                         8.0.1
dwave-optimization                      0.3.0
dwave-preprocessing                     0.6.6
dwave-samplers                          1.3.0
dwave-system                            1.26.0
dwave-tabu                              0.5.0
dwavebinarycsp                          0.3.0
ecos                                    2.0.14
einops                                  0.8.0
einx                                    0.3.0
EMD-signal                              1.6.4
empyrical-reloaded                      0.5.11
en-core-web-md                          3.7.1
en-core-web-sm                          3.7.1
et_xmlfile                              2.0.0
etuples                                 0.3.9
exchange_calendars                      4.6
executing                               2.1.0
faiss-cpu                               1.9.0.post1
Farama-Notifications                    0.0.4
fastai                                  2.7.18
fastai2                                 0.0.30
fastcore                                1.7.26
fastdownload                            0.0.7
fasteners                               0.19
fastjsonschema                          2.21.1
fastparquet                             2024.11.0
fastprogress                            1.0.3
fastrlock                               0.8.2
fasttext                                0.9.3
feature-engine                          1.6.2
featuretools                            1.31.0
filelock                                3.16.1
filetype                                1.2.0
findiff                                 0.10.2
FixedEffectModel                        0.0.5
FlagEmbedding                           1.2.11
Flask                                   3.1.0
flatbuffers                             24.3.25
fonttools                               4.55.2
formulaic                               1.0.2
fqdn                                    1.5.1
frozendict                              2.4.2
frozenlist                              1.5.0
fs                                      2.4.16
fsspec                                  2024.6.1
fugue                                   0.9.1
future                                  1.0.0
fuzzy-c-means                           1.7.2
gast                                    0.6.0
gatspy                                  0.3
gensim                                  4.3.3
geopandas                               1.0.1
gevent                                  24.11.1
gitdb                                   4.0.11
GitPython                               3.1.43
gluonts                                 0.16.0
google-ai-generativelanguage            0.6.10
google-api-core                         2.24.0
google-api-python-client                2.154.0
google-auth                             2.36.0
google-auth-httplib2                    0.2.0
google-generativeai                     0.8.3
google-pasta                            0.2.0
googleapis-common-protos                1.66.0
gpflow                                  2.9.2
gplearn                                 0.4.2
gpytorch                                1.13
graphene                                3.4.3
graphql-core                            3.2.5
graphql-relay                           3.2.0
graphviz                                0.20.3
greenlet                                3.1.1
grpcio                                  1.68.1
grpcio-status                           1.68.1
gunicorn                                23.0.0
gym                                     0.26.2
gym-notices                             0.0.8
gymnasium                               1.0.0
h11                                     0.14.0
h2o                                     3.46.0.6
h5netcdf                                1.4.1
h5py                                    3.12.1
hmmlearn                                0.3.3
holidays                                0.62
holoviews                               1.20.0
homebase                                1.0.1
hopcroftkarp                            1.2.5
html5lib                                1.1
httpcore                                1.0.7
httplib2                                0.22.0
httpstan                                4.13.0
httpx                                   0.28.1
huggingface-hub                         0.26.5
hurst                                   0.0.5
hvplot                                  0.11.1
hyperopt                                0.2.7
ibm-cloud-sdk-core                      3.22.0
ibm-platform-services                   0.59.0
idna                                    3.7
iisignature                             0.24
ijson                                   3.3.0
imageio                                 2.36.1
imbalanced-learn                        0.12.4
immutabledict                           4.2.1
importlib_metadata                      8.5.0
importlib_resources                     6.4.5
iniconfig                               2.0.0
injector                                0.22.0
interface-meta                          1.3.0
interpret                               0.6.7
interpret-core                          0.6.7
ipykernel                               6.29.5
ipympl                                  0.9.4
ipython                                 8.30.0
ipython-genutils                        0.2.0
ipywidgets                              8.1.5
isoduration                             20.11.0
itsdangerous                            2.2.0
jax                                     0.4.35
jaxlib                                  0.4.35
jaxtyping                               0.2.19
jedi                                    0.19.2
Jinja2                                  3.1.4
jiter                                   0.8.2
joblib                                  1.3.2
json5                                   0.10.0
jsonpatch                               1.33
jsonpath-ng                             1.7.0
jsonpointer                             2.1
jsonschema                              4.23.0
jsonschema-specifications               2024.10.1
jupyter                                 1.1.1
jupyter_ai                              2.28.2
jupyter_ai_magics                       2.28.3
jupyter_bokeh                           4.0.5
jupyter_client                          8.6.3
jupyter-console                         6.6.3
jupyter_core                            5.7.2
jupyter-events                          0.10.0
jupyter-lsp                             2.2.5
jupyter-resource-usage                  1.1.0
jupyter_server                          2.14.2
jupyter_server_proxy                    4.4.0
jupyter_server_terminals                0.5.3
jupyterlab                              4.3.2
jupyterlab_pygments                     0.3.0
jupyterlab_server                       2.27.3
jupyterlab_widgets                      3.0.13
kagglehub                               0.3.4
kaleido                                 0.2.1
keras                                   3.7.0
keras-hub                               0.18.1
keras-nlp                               0.18.1
keras-rl                                0.4.2
keras-tcn                               3.5.0
keras-tuner                             1.4.7
kiwisolver                              1.4.7
kmapper                                 2.1.0
korean-lunar-calendar                   0.3.1
kt-legacy                               1.0.5
langchain                               0.2.17
langchain-community                     0.2.19
langchain-core                          0.2.43
langchain-text-splitters                0.2.4
langcodes                               3.5.0
langsmith                               0.1.147
language_data                           1.3.0
lark                                    1.2.2
lazy_loader                             0.4
lazypredict                             0.2.14a1
libclang                                18.1.1
libmambapy                              1.5.8
libucx-cu12                             1.15.0.post2
lifelines                               0.30.0
lightgbm                                4.5.0
lightning                               2.4.0
lightning-utilities                     0.11.9
lime                                    0.2.0.1
line_profiler                           4.2.0
linear-operator                         0.5.3
linkify-it-py                           2.0.3
livelossplot                            0.5.5
llama-cloud                             0.1.6
llama-index                             0.12.2
llama-index-agent-openai                0.4.0
llama-index-cli                         0.4.0
llama-index-core                        0.12.5
llama-index-embeddings-openai           0.3.1
llama-index-indices-managed-llama-cloud 0.6.3
llama-index-legacy                      0.9.48.post4
llama-index-llms-openai                 0.3.3
llama-index-multi-modal-llms-openai     0.3.0
llama-index-program-openai              0.3.1
llama-index-question-gen-openai         0.3.0
llama-index-readers-file                0.4.1
llama-index-readers-llama-parse         0.4.0
llama-parse                             0.5.17
llvmlite                                0.42.0
locket                                  1.0.0
logical-unification                     0.4.6
loguru                                  0.7.3
lxml                                    5.3.0
lz4                                     4.3.3
Mako                                    1.3.8
mamba-ssm                               2.2.4
MAPIE                                   0.9.1
marisa-trie                             1.2.1
Markdown                                3.7
markdown-it-py                          3.0.0
MarkupSafe                              3.0.2
marshmallow                             3.23.1
matplotlib                              3.7.5
matplotlib-inline                       0.1.7
mdit-py-plugins                         0.4.2
mdurl                                   0.1.2
menuinst                                2.1.2
mgarch                                  0.3.0
miniKanren                              1.0.3
minorminer                              0.2.15
mistune                                 3.0.2
ml-dtypes                               0.4.1
mlflow                                  2.18.0
mlflow-skinny                           2.18.0
mlforecast                              0.15.1
mljar-scikit-plot                       0.3.12
mljar-supervised                        1.1.9
mlxtend                                 0.23.3
mmh3                                    2.5.1
modin                                   0.26.1
mplfinance                              0.12.10b0
mpmath                                  1.3.0
msgpack                                 1.1.0
multidict                               6.1.0
multipledispatch                        1.0.0
multiprocess                            0.70.16
multitasking                            0.0.11
murmurhash                              1.0.11
mypy-extensions                         1.0.0
namex                                   0.0.8
narwhals                                1.17.0
nbclient                                0.10.1
nbconvert                               7.16.4
nbformat                                5.10.4
ndindex                                 1.9.2
nest-asyncio                            1.6.0
networkx                                3.4.2
neural-tangents                         0.6.5
neuralprophet                           0.9.0
nfoursid                                1.0.1
ngboost                                 0.5.1
ninja                                   1.11.1.2
nixtla                                  0.6.4
nltk                                    3.9.1
nolds                                   0.6.1
nose                                    1.3.7
notebook                                7.3.1
notebook_shim                           0.2.4
numba                                   0.59.1
numerapi                                2.19.1
numexpr                                 2.10.2
numpy                                   1.26.4
nvidia-cublas-cu12                      12.4.5.8
nvidia-cuda-cupti-cu12                  12.4.127
nvidia-cuda-nvrtc-cu12                  12.4.127
nvidia-cuda-runtime-cu12                12.4.127
nvidia-cudnn-cu12                       9.3.0.75
nvidia-cufft-cu12                       11.2.1.3
nvidia-curand-cu12                      10.3.5.147
nvidia-cusolver-cu12                    11.6.1.9
nvidia-cusparse-cu12                    12.3.1.170
nvidia-nccl-cu12                        2.21.5
nvidia-nvjitlink-cu12                   12.4.127
nvidia-nvtx-cu12                        12.4.127
nvtx                                    0.2.10
nx-cugraph-cu12                         24.8.0
oauthlib                                3.2.2
openai                                  1.57.0
opencv-contrib-python-headless          4.10.0.84
opencv-python                           4.10.0.84
openpyxl                                3.1.5
opentelemetry-api                       1.28.2
opentelemetry-sdk                       1.28.2
opentelemetry-semantic-conventions      0.49b2
opt_einsum                              3.4.0
optree                                  0.13.1
optuna                                  4.1.0
orjson                                  3.10.12
ortools                                 9.9.3963
osqp                                    0.6.7.post3
overrides                               7.7.0
packaging                               24.1
pandas                                  2.1.4
pandas-flavor                           0.6.0
pandas_market_calendars                 4.4.2
pandas_ta                               0.3.14b0
pandocfilters                           1.5.1
panel                                   1.5.4
param                                   2.1.1
parso                                   0.8.4
partd                                   1.4.2
pastel                                  0.2.1
pathos                                  0.3.2
patsy                                   1.0.1
pbr                                     6.1.0
peewee                                  3.17.3
peft                                    0.13.2
penaltymodel                            1.1.0
PennyLane                               0.39.0
PennyLane_Lightning                     0.39.0
PennyLane-qiskit                        0.36.0
persim                                  0.3.7
pexpect                                 4.9.0
pgmpy                                   0.1.26
pillow                                  10.4.0
pingouin                                0.5.5
pip                                     24.2
platformdirs                            3.10.0
plotly                                  5.24.1
plotly-resampler                        0.10.0
plucky                                  0.4.3
pluggy                                  1.5.0
ply                                     3.11
pmdarima                                2.0.4
polars                                  1.16.0
pomegranate                             1.1.1
POT                                     0.9.5
pox                                     0.3.5
ppft                                    1.7.6.9
pprofile                                2.2.0
preshed                                 3.0.9
prometheus_client                       0.21.1
prompt_toolkit                          3.0.48
propcache                               0.2.1
prophet                                 1.1.6
proto-plus                              1.25.0
protobuf                                5.29.1
psutil                                  5.9.8
ptyprocess                              0.7.0
PuLP                                    2.9.0
pure_eval                               0.2.3
py-cpuinfo                              9.0.0
py-heat                                 0.0.6
py-heat-magic                           0.0.2
py_lets_be_rational                     1.0.1
py_vollib                               1.0.1
py4j                                    0.10.9.7
pyaml                                   24.9.0
pyarrow                                 16.1.0
pyasn1                                  0.6.1
pyasn1_modules                          0.4.1
pybind11                                2.13.6
pycaret                                 3.3.2
pycosat                                 0.6.6
pycparser                               2.21
pyct                                    0.5.0
pydantic                                2.9.2
pydantic_core                           2.23.4
PyDMD                                   2024.12.1
pyerfa                                  2.0.1.5
pyfolio-reloaded                        0.9.8
Pygments                                2.18.0
PyJWT                                   2.10.1
pykalman                                0.9.7
pylev                                   1.4.0
pylibcugraph-cu12                       24.8.0
pylibraft-cu12                          24.8.1
pyluach                                 2.2.0
pymannkendall                           1.4.3
pymc                                    5.19.0
pymdptoolbox                            4.0b3
pynndescent                             0.5.13
pynvjitlink-cu12                        0.4.0
pynvml                                  11.4.1
pyod                                    2.0.2
pyogrio                                 0.10.0
Pyomo                                   6.8.2
pyparsing                               3.2.0
pypdf                                   5.1.0
pyportfolioopt                          1.5.6
pyproj                                  3.7.0
PyQt6                                   6.7.1
PyQt6-Qt6                               6.7.3
PyQt6_sip                               13.9.0
pyrb                                    1.0.1
pyre-extensions                         0.0.32
pyro-api                                0.1.2
pyro-ppl                                1.9.1
pysimdjson                              6.0.2
PySocks                                 1.7.1
pyspnego                                0.11.2
pystan                                  3.10.0
pytensor                                2.26.4
pytest                                  8.3.4
pytest-runner                           6.0.1
python-dateutil                         2.9.0.post0
python-json-logger                      2.0.7
python-statemachine                     2.5.0
pytorch-forecasting                     1.2.0
pytorch-ignite                          0.5.1
pytorch-lightning                       2.4.0
pytorch-tabnet                          4.1.0
pytz                                    2024.2
pyvinecopulib                           0.6.5
pyviz_comms                             3.0.3
PyWavelets                              1.7.0
PyYAML                                  6.0.2
pyzmq                                   26.2.0
qdldl                                   0.1.7.post4
qiskit                                  1.2.4
qiskit-aer                              0.15.1
qiskit-ibm-provider                     0.11.0
qiskit-ibm-runtime                      0.34.0
quadprog                                0.1.13
quantecon                               0.7.2
QuantLib                                1.36
QuantStats                              0.0.64
raft-dask-cu12                          24.8.1
rapids-dask-dependency                  24.8.0
rauth                                   0.7.3
ray                                     2.40.0
Rbeast                                  0.1.23
referencing                             0.35.1
regex                                   2024.11.6
requests                                2.32.3
requests_ntlm                           1.3.0
requests-oauthlib                       1.3.1
requests-toolbelt                       1.0.0
rfc3339-validator                       0.1.4
rfc3986-validator                       0.1.1
rich                                    13.9.4
ripser                                  0.6.10
Riskfolio-Lib                           6.1.1
riskparityportfolio                     0.6.0
river                                   0.21.0
rmm-cu12                                24.8.2
rpds-py                                 0.22.3
rsa                                     4.9
ruamel.yaml                             0.18.6
ruamel.yaml.clib                        0.2.8
ruptures                                1.1.9
rustworkx                               0.15.1
safetensors                             0.4.5
SALib                                   1.5.1
schemdraw                               0.15
scikeras                                0.13.0
scikit-base                             0.7.8
scikit-image                            0.22.0
scikit-learn                            1.4.2
scikit-learn-extra                      0.3.0
scikit-optimize                         0.10.2
scikit-plot                             0.3.7
scikit-tda                              1.1.1
scipy                                   1.11.4
scs                                     3.2.7
sdeint                                  0.3.0
seaborn                                 0.13.2
Send2Trash                              1.8.3
sentence-transformers                   3.3.1
setuptools                              73.0.1
setuptools-scm                          8.1.0
shap                                    0.46.0
shapely                                 2.0.6
Shimmy                                  2.0.0
simpervisor                             1.0.0
simplejson                              3.19.3
simpy                                   4.1.1
six                                     1.17.0
sklearn-json                            0.1.0
sktime                                  0.26.0
slicer                                  0.0.8
smart-open                              7.0.5
smmap                                   5.0.1
sniffio                                 1.3.1
sortedcontainers                        2.4.0
soupsieve                               2.6
spacy                                   3.7.5
spacy-legacy                            3.0.12
spacy-loggers                           1.0.5
SQLAlchemy                              2.0.36
sqlparse                                0.5.3
srsly                                   2.5.0
ssm                                     0.0.1
stable_baselines3                       2.4.0
stack-data                              0.6.3
stanio                                  0.5.1
statsforecast                           2.0.0
statsmodels                             0.14.4
stevedore                               5.4.0
stochastic                              0.6.0
stockstats                              0.6.2
stopit                                  1.1.2
striprtf                                0.0.26
stumpy                                  1.13.0
symengine                               0.13.0
sympy                                   1.13.1
ta                                      0.11.0
ta-lib                                  0.5.1
tables                                  3.10.1
tabulate                                0.8.10
tadasets                                0.2.1
tbats                                   1.1.3
tblib                                   3.0.0
tenacity                                8.5.0
tensorboard                             2.18.0
tensorboard-data-server                 0.7.2
tensorboardX                            2.6.2.2
tensorflow                              2.18.0
tensorflow-addons                       0.23.0
tensorflow_decision_forests             1.11.0
tensorflow-io-gcs-filesystem            0.37.1
tensorflow-probability                  0.25.0
tensorflow-text                         2.18.0
tensorly                                0.9.0
tensorrt                                10.7.0
tensorrt_cu12                           10.7.0
tensorrt-cu12-bindings                  10.7.0
tensorrt-cu12-libs                      10.7.0
tensortrade                             1.0.3
termcolor                               2.5.0
terminado                               0.18.1
tf_keras                                2.18.0
tf2jax                                  0.3.6
thinc                                   8.2.5
threadpoolctl                           3.5.0
thundergbm                              0.3.17
tifffile                                2024.9.20
tigramite                               5.2.6.7
tiktoken                                0.8.0
tinycss2                                1.4.0
tinygrad                                0.10.0
tokenizers                              0.20.3
toml                                    0.10.2
toolz                                   0.12.1
torch                                   2.5.1
torch_cluster                           1.6.3
torch-geometric                         2.6.1
torch_scatter                           2.1.2
torch_sparse                            0.6.18
torch_spline_conv                       1.2.2
torchdata                               0.10.0
torchmetrics                            1.6.0
torchvision                             0.20.1
tornado                                 6.4.2
TPOT                                    0.12.2
tqdm                                    4.66.5
traitlets                               5.14.3
transformers                            4.46.3
treelite                                4.3.0
triad                                   0.9.8
triton                                  3.1.0
truststore                              0.8.0
tsdownsample                            0.1.3
tsfel                                   0.1.9
tsfresh                                 0.20.2
tslearn                                 0.6.3
tweepy                                  4.14.0
typeguard                               2.13.3
typer                                   0.9.4
typer-config                            1.4.2
types-python-dateutil                   2.9.0.20241206
typing_extensions                       4.12.2
typing-inspect                          0.9.0
tzdata                                  2024.2
uc-micro-py                             1.0.3
ucx-py-cu12                             0.39.2
ucxx-cu12                               0.39.1
umap-learn                              0.5.7
update-checker                          0.18.0
uri-template                            1.3.0
uritemplate                             4.1.1
urllib3                                 2.2.3
utilsforecast                           0.2.10
wasabi                                  1.1.3
wcwidth                                 0.2.13
weasel                                  0.4.1
webargs                                 8.6.0
webcolors                               24.11.1
webencodings                            0.5.1
websocket-client                        1.8.0
websockets                              14.1
Werkzeug                                3.1.3
wheel                                   0.44.0
widgetsnbextension                      4.0.13
window_ops                              0.0.15
woodwork                                0.31.0
wordcloud                               1.9.4
wrapt                                   1.16.0
wurlitzer                               3.1.1
x-transformers                          1.42.24
xarray                                  2024.11.0
xarray-einstats                         0.8.0
xgboost                                 2.1.3
xlrd                                    2.0.1
XlsxWriter                              3.2.0
xxhash                                  3.5.0
xyzservices                             2024.9.0
yarl                                    1.18.3
ydf                                     0.9.0
yellowbrick                             1.5
yfinance                                0.2.50
zict                                    3.0.0
zipp                                    3.21.0
zope.event                              5.0
zope.interface                          7.2
zstandard                               0.23.0

Accord                               3.6.0
Accord.Fuzzy                         3.6.0
Accord.MachineLearning               3.6.0
Accord.Math                          3.6.0
Accord.Statistics                    3.6.0
CloneExtensions                      1.3.0
Common.Logging                       3.4.1
Common.Logging.Core                  3.4.1
CsvHelper                            19.0.0
Deedle                               2.1.0
DotNetZip                            1.16.0
DynamicInterop                       0.9.1
fasterflect                          3.0.0
MathNet.Numerics                     5.0.0
McMaster.Extensions.CommandLineUtils 2.6.0
Microsoft.IO.RecyclableMemoryStream  2.3.2
Microsoft.NET.Test.Sdk               16.9.4
Microsoft.TestPlatform.ObjectModel   16.9.4
Moq                                  4.16.1
NetMQ                                4.0.1.6
Newtonsoft.Json                      13.0.2
NodaTime                             3.0.5
NUnit                                4.2.2
NUnit3TestAdapter                    4.6.0
protobuf-net                         3.1.3
QLNet                                1.13.0
QuantConnect.pythonnet               2.0.42
RestSharp                            106.12.0
SharpZipLib                          1.3.3
System.ComponentModel.Composition    6.0.0
Accord.Audio                         3.6.0
Accord.Genetic                       3.6.0
Accord.MachineLearning.GPL           3.6.0
Catalyst                             1.0.54164
Catalyst.Models.English              1.0.30952
CNTK.CPUOnly                         2.8.0-rc0.dev20200201
csnumerics                           1.0.2
Google.OrTools                       9.11.4210
LibTopoART                           0.98.0
MathNet.Filtering                    0.7.0
MathNet.Filtering.Kalman             0.7.0
MathNet.Spatial                      0.6.0
Microsoft.Data.Analysis              0.22.0
Microsoft.ML                         4.0.0
Microsoft.ML.CpuMath                 4.0.0
Microsoft.ML.DataView                4.0.0
Microsoft.ML.Ensemble                0.22.0
Microsoft.ML.FastTree                4.0.0
Microsoft.ML.LightGbm                4.0.0
Microsoft.ML.Mkl.Components          4.0.0
Microsoft.ML.OnnxRuntime             1.20.1
Microsoft.ML.TensorFlow              4.0.0
Microsoft.ML.TimeSeries              4.0.0
NumSharp                             0.30.0
OpenAI                               2.1.0
Plotly.NET                           5.1.0
Plotly.NET.CSharp                    0.13.0
Plotly.NET.Interactive               5.0.0
QuantConnect.PredictNowNET           1.0.2
R.NET                                1.9.0
SciSharp.TensorFlow.Redist           2.16.0
SciSharp.TensorFlow.Redist-Linux-GPU 2.11.1
SharpLearning.AdaBoost               0.31.8
SharpLearning.Common.Interfaces      0.31.8
SharpLearning.Containers             0.31.8
SharpLearning.CrossValidation        0.31.8
SharpLearning.DecisionTrees          0.31.8
SharpLearning.Ensemble               0.31.8
SharpLearning.FeatureTransformations 0.31.8
SharpLearning.GradientBoost          0.31.8
SharpLearning.InputOutput            0.31.8
SharpLearning.Metrics                0.31.8
SharpLearning.Neural                 0.31.8
SharpLearning.Optimization           0.31.8
SharpLearning.RandomForest           0.31.8
SharpLearning.XGBoost                0.31.8
SharpNeatLib                         2.4.4
TensorFlow.Keras                     0.15.0
TensorFlow.NET                       0.150.0

Legacy Environment

This environment is only available for Python.

The Legacy environment provides a backwards compatability environment for compatibility with Keras < 3 , Tensorflow 2.14.1 , and Pydantic . This environment supports the following libraries:

absl-py                                 2.1.0
accelerate                              0.34.2
adagio                                  0.2.6
aesara                                  2.9.4
aiohappyeyeballs                        2.4.4
aiohttp                                 3.11.10
aiosignal                               1.3.1
aiosqlite                               0.20.0
alembic                                 1.14.0
alibi-detect                            0.12.0
alphalens-reloaded                      0.4.5
altair                                  5.5.0
anaconda-anon-usage                     0.4.4
annotated-types                         0.7.0
antlr4-python3-runtime                  4.9.3
anyio                                   4.7.0
aplr                                    10.8.0
appdirs                                 1.4.4
apricot-select                          0.6.1
arch                                    7.2.0
archspec                                0.2.3
argon2-cffi                             23.1.0
argon2-cffi-bindings                    21.2.0
arrow                                   1.3.0
arviz                                   0.20.0
astropy                                 7.0.0
astropy-iers-data                       0.2024.12.9.0.36.21
asttokens                               3.0.0
astunparse                              1.6.3
async-lru                               2.0.4
attrs                                   24.2.0
Authlib                                 1.3.2
autograd                                1.7.0
autograd-gamma                          0.5.0
autokeras                               2.0.0
autoray                                 0.7.0
ax-platform                             0.4.3
babel                                   2.16.0
bayesian-optimization                   2.0.0
beautifulsoup4                          4.12.3
bleach                                  6.2.0
blinker                                 1.9.0
blis                                    0.7.11
blosc2                                  2.7.1
bokeh                                   3.6.2
boltons                                 23.0.0
botorch                                 0.12.0
Bottleneck                              1.4.2
Brotli                                  1.0.9
cachetools                              5.5.0
captum                                  0.7.0
catalogue                               2.0.10
catboost                                1.2.7
category-encoders                       2.6.4
causal-conv1d                           1.5.0.post8
certifi                                 2024.8.30
cesium                                  0.12.1
cffi                                    1.17.1
chardet                                 5.2.0
charset-normalizer                      3.3.2
check-shapes                            1.1.1
chronos-forecasting                     1.4.1
clarabel                                0.9.0
click                                   8.1.7
clikit                                  0.6.2
cloudpathlib                            0.20.0
cloudpickle                             3.1.0
cmdstanpy                               1.2.4
colorama                                0.4.6
colorcet                                3.1.0
colorlog                                6.9.0
colorlover                              0.3.0
colour                                  0.1.5
comm                                    0.2.2
conda                                   24.9.2
conda-content-trust                     0.2.0
conda-libmamba-solver                   24.9.0
conda-package-handling                  2.3.0
conda_package_streaming                 0.10.0
confection                              0.1.5
cons                                    0.4.6
contourpy                               1.3.1
control                                 0.10.1
copulae                                 0.7.9
copulas                                 0.12.0
coreforecast                            0.0.15
cramjam                                 2.9.0
crashtest                               0.3.1
creme                                   0.6.1
cryptography                            43.0.0
cucim-cu12                              24.8.0
cuda-python                             12.6.2.post1
cudf-cu12                               24.8.3
cufflinks                               0.17.3
cugraph-cu12                            24.8.0
cuml-cu12                               24.8.0
cuproj-cu12                             24.8.0
cupy-cuda12x                            13.3.0
cuspatial-cu12                          24.8.0
cuvs-cu12                               24.8.0
cuxfilter-cu12                          24.8.0
cvxopt                                  1.3.2
cvxportfolio                            1.4.0
cvxpy                                   1.6.0
cycler                                  0.12.1
cymem                                   2.0.10
Cython                                  3.0.11
darts                                   0.31.0
dash                                    2.9.3
dash-core-components                    2.0.0
dash_cytoscape                          1.0.2
dash-html-components                    2.0.0
dash-table                              5.0.0
dask                                    2024.7.1
dask-cuda                               24.8.2
dask-cudf-cu12                          24.8.3
dask-expr                               1.1.9
databricks-sdk                          0.38.0
dataclasses-json                        0.6.7
datasets                                2.17.1
datashader                              0.16.3
deap                                    1.4.1
debugpy                                 1.8.9
decorator                               5.1.1
deepmerge                               2.0
defusedxml                              0.7.1
Deprecated                              1.2.15
deprecation                             2.1.0
dgl                                     2.1.0
dill                                    0.3.8
dimod                                   0.12.17
dirtyjson                               1.0.8
diskcache                               5.6.3
distributed                             2024.7.1
distributed-ucxx-cu12                   0.39.1
distro                                  1.9.0
dm-tree                                 0.1.8
docker                                  7.1.0
docutils                                0.21.2
DoubleML                                0.9.0
dropstackframe                          0.1.1
dtreeviz                                2.2.2
dtw-python                              1.5.3
dwave-cloud-client                      0.13.1
dwave-drivers                           0.4.4
dwave-gate                              0.3.2
dwave-greedy                            0.3.0
dwave-hybrid                            0.6.12
dwave-inspector                         0.5.1
dwave-inspectorapp                      0.3.3
dwave-neal                              0.6.0
dwave_networkx                          0.8.15
dwave-ocean-sdk                         8.0.1
dwave-optimization                      0.3.0
dwave-preprocessing                     0.6.6
dwave-samplers                          1.3.0
dwave-system                            1.26.0
dwave-tabu                              0.5.0
dwavebinarycsp                          0.3.0
ecos                                    2.0.14
einops                                  0.7.0
einx                                    0.3.0
EMD-signal                              1.6.4
empyrical-reloaded                      0.5.11
en-core-web-md                          3.7.1
en-core-web-sm                          3.7.1
et_xmlfile                              2.0.0
etuples                                 0.3.9
exchange_calendars                      4.6
executing                               2.1.0
faiss-cpu                               1.9.0.post1
Farama-Notifications                    0.0.4
fastai                                  2.7.18
fastai2                                 0.0.30
fastcore                                1.7.26
fastdownload                            0.0.7
fasteners                               0.19
fastjsonschema                          2.21.1
fastparquet                             2024.11.0
fastprogress                            1.0.3
fastrlock                               0.8.2
fasttext                                0.9.3
feature-engine                          1.6.2
featuretools                            1.31.0
filelock                                3.16.1
filetype                                1.2.0
findiff                                 0.10.2
FixedEffectModel                        0.0.5
FlagEmbedding                           1.2.11
FLAML                                   2.3.3
Flask                                   3.1.0
flatbuffers                             24.3.25
fonttools                               4.55.2
formulaic                               1.0.2
fqdn                                    1.5.1
frozendict                              2.4.2
frozenlist                              1.5.0
fs                                      2.4.16
fsspec                                  2023.10.0
fugue                                   0.9.1
functime                                0.9.5
future                                  1.0.0
fuzzy-c-means                           1.7.2
gast                                    0.6.0
gatspy                                  0.3
gensim                                  4.3.3
geopandas                               1.0.1
gevent                                  24.11.1
gitdb                                   4.0.11
GitPython                               3.1.43
gluonts                                 0.14.4
google-ai-generativelanguage            0.6.10
google-api-core                         2.24.0
google-api-python-client                2.154.0
google-auth                             2.36.0
google-auth-httplib2                    0.2.0
google-auth-oauthlib                    1.0.0
google-generativeai                     0.8.3
google-pasta                            0.2.0
googleapis-common-protos                1.66.0
gpflow                                  2.9.2
gplearn                                 0.4.2
gpytorch                                1.13
graphene                                3.4.3
graphql-core                            3.2.5
graphql-relay                           3.2.0
graphviz                                0.20.3
greenlet                                3.1.1
grpcio                                  1.68.1
grpcio-status                           1.68.1
gunicorn                                23.0.0
gym                                     0.26.2
gym-notices                             0.0.8
gymnasium                               1.0.0
h11                                     0.14.0
h2o                                     3.46.0.6
h5netcdf                                1.4.1
h5py                                    3.12.1
hmmlearn                                0.3.3
holidays                                0.62
holoviews                               1.20.0
homebase                                1.0.1
hopcroftkarp                            1.2.5
html5lib                                1.1
httpcore                                1.0.7
httplib2                                0.22.0
httpstan                                4.13.0
httpx                                   0.28.1
huggingface-hub                         0.26.5
hurst                                   0.0.5
hvplot                                  0.11.1
hydra-core                              1.3.0
hyperopt                                0.2.7
ibm-cloud-sdk-core                      3.22.0
ibm-platform-services                   0.59.0
idna                                    3.7
iisignature                             0.24
ijson                                   3.3.0
imageio                                 2.36.1
imbalanced-learn                        0.12.4
immutabledict                           4.2.1
importlib-metadata                      4.13.0
importlib_resources                     6.4.5
iniconfig                               2.0.0
injector                                0.22.0
interface-meta                          1.3.0
interpret                               0.6.7
interpret-core                          0.6.7
ipykernel                               6.29.5
ipympl                                  0.9.4
ipython                                 8.30.0
ipython-genutils                        0.2.0
ipywidgets                              8.1.5
isoduration                             20.11.0
itsdangerous                            2.2.0
jax                                     0.4.34
jaxlib                                  0.4.34
jaxtyping                               0.2.36
jedi                                    0.19.2
Jinja2                                  3.1.4
jiter                                   0.8.2
joblib                                  1.3.2
json5                                   0.10.0
jsonpatch                               1.33
jsonpath-ng                             1.7.0
jsonpointer                             2.1
jsonschema                              4.23.0
jsonschema-specifications               2024.10.1
jupyter                                 1.1.1
jupyter_ai                              2.28.2
jupyter_ai_magics                       2.28.3
jupyter_bokeh                           4.0.5
jupyter_client                          8.6.3
jupyter-console                         6.6.3
jupyter_core                            5.7.2
jupyter-events                          0.10.0
jupyter-lsp                             2.2.5
jupyter-resource-usage                  1.1.0
jupyter_server                          2.14.2
jupyter_server_proxy                    4.4.0
jupyter_server_terminals                0.5.3
jupyterlab                              4.3.2
jupyterlab_pygments                     0.3.0
jupyterlab_server                       2.27.3
jupyterlab_widgets                      3.0.13
kagglehub                               0.3.4
kaleido                                 0.2.1
keras                                   2.14.0
keras-hub                               0.18.1
keras-nlp                               0.18.1
keras-rl                                0.4.2
keras-tcn                               3.5.0
keras-tuner                             1.4.7
kiwisolver                              1.4.7
kmapper                                 2.1.0
korean-lunar-calendar                   0.3.1
kt-legacy                               1.0.5
langchain                               0.2.17
langchain-community                     0.2.19
langchain-core                          0.2.43
langchain-text-splitters                0.2.4
langcodes                               3.5.0
langsmith                               0.1.147
language_data                           1.3.0
lark                                    1.2.2
lazy_loader                             0.4
lazypredict                             0.2.14a1
libclang                                18.1.1
libmambapy                              1.5.8
libucx-cu12                             1.15.0.post2
lifelines                               0.30.0
lightgbm                                4.5.0
lightning                               2.4.0
lightning-utilities                     0.11.9
lime                                    0.2.0.1
line_profiler                           4.2.0
linear-operator                         0.5.3
linkify-it-py                           2.0.3
livelossplot                            0.5.5
llama-cloud                             0.1.6
llama-index                             0.12.2
llama-index-agent-openai                0.4.0
llama-index-cli                         0.4.0
llama-index-core                        0.12.5
llama-index-embeddings-openai           0.3.1
llama-index-indices-managed-llama-cloud 0.6.3
llama-index-legacy                      0.9.48.post4
llama-index-llms-openai                 0.3.3
llama-index-multi-modal-llms-openai     0.3.0
llama-index-program-openai              0.3.1
llama-index-question-gen-openai         0.3.0
llama-index-readers-file                0.4.1
llama-index-readers-llama-parse         0.4.0
llama-parse                             0.5.17
llvmlite                                0.42.0
locket                                  1.0.0
logical-unification                     0.4.6
loguru                                  0.7.3
lxml                                    5.3.0
lz4                                     4.3.3
Mako                                    1.3.8
mamba-ssm                               2.2.4
MAPIE                                   0.9.1
marisa-trie                             1.2.1
Markdown                                3.7
markdown-it-py                          3.0.0
MarkupSafe                              3.0.2
marshmallow                             3.23.1
matplotlib                              3.7.5
matplotlib-inline                       0.1.7
mdit-py-plugins                         0.4.2
mdurl                                   0.1.2
menuinst                                2.1.2
mgarch                                  0.3.0
miniKanren                              1.0.3
minorminer                              0.2.15
mistune                                 3.0.2
ml-dtypes                               0.2.0
mlflow                                  2.18.0
mlflow-skinny                           2.18.0
mlforecast                              0.15.1
mljar-scikit-plot                       0.3.12
mljar-supervised                        1.1.9
mlxtend                                 0.23.3
mmh3                                    2.5.1
modin                                   0.26.1
mplfinance                              0.12.10b0
mpmath                                  1.3.0
msgpack                                 1.1.0
multidict                               6.1.0
multipledispatch                        1.0.0
multiprocess                            0.70.16
multitasking                            0.0.11
murmurhash                              1.0.11
mypy-extensions                         1.0.0
namex                                   0.0.8
narwhals                                1.17.0
nbclient                                0.10.1
nbconvert                               7.16.4
nbformat                                5.10.4
ndindex                                 1.9.2
nest-asyncio                            1.6.0
networkx                                3.4.2
neural-tangents                         0.6.5
neuralprophet                           0.9.0
nfoursid                                1.0.1
ngboost                                 0.5.1
ninja                                   1.11.1.2
nixtla                                  0.6.4
nltk                                    3.9.1
nolds                                   0.6.1
nose                                    1.3.7
notebook                                7.3.1
notebook_shim                           0.2.4
numba                                   0.59.1
numerapi                                2.19.1
numexpr                                 2.10.2
numpy                                   1.26.4
nvidia-cublas-cu12                      12.1.3.1
nvidia-cuda-cupti-cu12                  12.1.105
nvidia-cuda-nvrtc-cu12                  12.1.105
nvidia-cuda-runtime-cu12                12.1.105
nvidia-cudnn-cu12                       9.1.0.70
nvidia-cufft-cu12                       11.0.2.54
nvidia-curand-cu12                      10.3.2.106
nvidia-cusolver-cu12                    11.4.5.107
nvidia-cusparse-cu12                    12.1.0.106
nvidia-nccl-cu12                        2.20.5
nvidia-nvjitlink-cu12                   12.4.127
nvidia-nvtx-cu12                        12.1.105
nvtx                                    0.2.10
nx-cugraph-cu12                         24.8.0
oauthlib                                3.2.2
omegaconf                               2.3.0
openai                                  1.57.0
opencv-contrib-python-headless          4.10.0.84
opencv-python                           4.10.0.84
openpyxl                                3.1.5
opentelemetry-api                       1.28.2
opentelemetry-sdk                       1.28.2
opentelemetry-semantic-conventions      0.49b2
opt_einsum                              3.4.0
optree                                  0.13.1
optuna                                  4.1.0
orjson                                  3.10.12
ortools                                 9.9.3963
osqp                                    0.6.7.post3
overrides                               7.7.0
packaging                               24.1
pandas                                  2.1.4
pandas-flavor                           0.6.0
pandas_market_calendars                 4.4.2
pandas_ta                               0.3.14b0
pandocfilters                           1.5.1
panel                                   1.5.4
param                                   2.1.1
parso                                   0.8.4
partd                                   1.4.2
pastel                                  0.2.1
pathos                                  0.3.2
patsy                                   1.0.1
pbr                                     6.1.0
pearl                                   2.3.12
peewee                                  3.17.3
peft                                    0.13.2
penaltymodel                            1.1.0
PennyLane                               0.39.0
PennyLane_Lightning                     0.39.0
PennyLane-qiskit                        0.36.0
persim                                  0.3.7
pexpect                                 4.9.0
pgmpy                                   0.1.26
pillow                                  10.4.0
pingouin                                0.5.5
pip                                     24.0
platformdirs                            3.10.0
plotly                                  5.24.1
plotly-resampler                        0.10.0
plucky                                  0.4.3
pluggy                                  1.5.0
ply                                     3.11
pmdarima                                2.0.4
polars                                  0.20.31
pomegranate                             1.1.1
POT                                     0.9.5
pox                                     0.3.5
ppft                                    1.7.6.9
pprofile                                2.2.0
preshed                                 3.0.9
prometheus_client                       0.21.1
prompt_toolkit                          3.0.48
propcache                               0.2.1
prophet                                 1.1.6
proto-plus                              1.25.0
protobuf                                4.25.5
psutil                                  5.9.8
ptyprocess                              0.7.0
PuLP                                    2.9.0
pure_eval                               0.2.3
py-cpuinfo                              9.0.0
py-heat                                 0.0.6
py-heat-magic                           0.0.2
py_lets_be_rational                     1.0.1
py_vollib                               1.0.1
py4j                                    0.10.9.7
pyaml                                   24.9.0
pyarrow                                 16.1.0
pyarrow-hotfix                          0.6
pyasn1                                  0.6.1
pyasn1_modules                          0.4.1
pybind11                                2.13.6
pycaret                                 3.3.2
pycosat                                 0.6.6
pycparser                               2.21
pyct                                    0.5.0
pydantic                                2.9.2
pydantic_core                           2.23.4
PyDMD                                   2024.12.1
pyerfa                                  2.0.1.5
pyfolio-reloaded                        0.9.8
Pygments                                2.18.0
PyJWT                                   2.10.1
pykalman                                0.9.7
pylev                                   1.4.0
pylibcugraph-cu12                       24.8.0
pylibraft-cu12                          24.8.1
pyluach                                 2.2.0
pymannkendall                           1.4.3
pymc                                    5.19.0
pymdptoolbox                            4.0b3
pynndescent                             0.5.13
pynvjitlink-cu12                        0.4.0
pynvml                                  11.4.1
pyod                                    2.0.2
pyogrio                                 0.10.0
Pyomo                                   6.8.2
pyparsing                               3.2.0
pypdf                                   5.1.0
pyportfolioopt                          1.5.6
pyproj                                  3.7.0
PyQt6                                   6.7.1
PyQt6-Qt6                               6.7.3
PyQt6_sip                               13.9.0
pyrb                                    1.0.1
pyre-extensions                         0.0.32
pyro-api                                0.1.2
pyro-ppl                                1.9.1
pysimdjson                              6.0.2
PySocks                                 1.7.1
pyspnego                                0.11.2
pystan                                  3.10.0
pytensor                                2.26.4
pytest                                  8.3.4
pytest-runner                           6.0.1
python-dateutil                         2.9.0.post0
python-dotenv                           1.0.0
python-json-logger                      2.0.7
python-statemachine                     2.5.0
pytorch-forecasting                     1.2.0
pytorch-ignite                          0.5.1
pytorch-lightning                       2.4.0
pytorch-tabnet                          4.1.0
pytz                                    2024.2
pyvinecopulib                           0.6.5
pyviz_comms                             3.0.3
PyWavelets                              1.7.0
PyYAML                                  6.0.2
pyzmq                                   26.2.0
qdldl                                   0.1.7.post4
qiskit                                  1.2.4
qiskit-aer                              0.15.1
qiskit-ibm-provider                     0.11.0
qiskit-ibm-runtime                      0.34.0
quadprog                                0.1.13
quantecon                               0.7.2
QuantLib                                1.36
QuantStats                              0.0.64
raft-dask-cu12                          24.8.1
rapids-dask-dependency                  24.8.0
rauth                                   0.7.3
ray                                     2.40.0
Rbeast                                  0.1.23
referencing                             0.35.1
regex                                   2024.11.6
requests                                2.32.3
requests_ntlm                           1.3.0
requests-oauthlib                       1.3.1
requests-toolbelt                       1.0.0
rfc3339-validator                       0.1.4
rfc3986-validator                       0.1.1
rich                                    13.9.4
ripser                                  0.6.10
Riskfolio-Lib                           6.1.1
riskparityportfolio                     0.6.0
river                                   0.21.0
rmm-cu12                                24.8.2
rpds-py                                 0.22.3
rsa                                     4.9
ruamel.yaml                             0.18.6
ruamel.yaml.clib                        0.2.8
ruptures                                1.1.9
rustworkx                               0.15.1
safetensors                             0.4.5
SALib                                   1.5.1
schemdraw                               0.15
scikeras                                0.13.0
scikit-base                             0.7.8
scikit-image                            0.22.0
scikit-learn                            1.4.2
scikit-learn-extra                      0.3.0
scikit-optimize                         0.10.2
scikit-plot                             0.3.7
scikit-tda                              1.1.1
scipy                                   1.11.4
scs                                     3.2.7
sdeint                                  0.3.0
seaborn                                 0.13.2
Send2Trash                              1.8.3
sentence-transformers                   3.3.1
setuptools                              65.5.0
setuptools-scm                          8.1.0
shap                                    0.46.0
shapely                                 2.0.6
Shimmy                                  2.0.0
simpervisor                             1.0.0
simplejson                              3.19.3
simpy                                   4.1.1
six                                     1.17.0
sklearn-json                            0.1.0
sktime                                  0.26.0
slicer                                  0.0.8
smart-open                              7.0.5
smmap                                   5.0.1
sniffio                                 1.3.1
sortedcontainers                        2.4.0
soupsieve                               2.6
spacy                                   3.7.5
spacy-legacy                            3.0.12
spacy-loggers                           1.0.5
SQLAlchemy                              2.0.36
sqlparse                                0.5.3
srsly                                   2.5.0
ssm                                     0.0.1
stable_baselines3                       2.4.0
stack-data                              0.6.3
stanio                                  0.5.1
statsforecast                           2.0.0
statsmodels                             0.14.4
stevedore                               5.4.0
stochastic                              0.6.0
stockstats                              0.6.2
stopit                                  1.1.2
striprtf                                0.0.26
stumpy                                  1.13.0
symengine                               0.13.0
sympy                                   1.13.1
ta                                      0.11.0
ta-lib                                  0.5.1
tables                                  3.10.1
tabulate                                0.8.10
tadasets                                0.2.1
tbats                                   1.1.3
tblib                                   3.0.0
tenacity                                8.5.0
tensorboard                             2.14.1
tensorboard-data-server                 0.7.2
tensorboardX                            2.6.2.2
tensorflow                              2.14.1
tensorflow-addons                       0.23.0
tensorflow_decision_forests             1.11.0
tensorflow-estimator                    2.14.0
tensorflow-io-gcs-filesystem            0.37.1
tensorflow-probability                  0.25.0
tensorflow-ranking                      0.5.3
tensorflow-serving-api                  2.14.1
tensorflow-text                         2.18.0
tensorly                                0.9.0
tensorrt                                10.7.0
tensorrt_cu12                           10.7.0
tensorrt-cu12-bindings                  10.7.0
tensorrt-cu12-libs                      10.7.0
tensortrade                             1.0.3
termcolor                               2.5.0
terminado                               0.18.1
tf_keras                                2.18.0
tf2jax                                  0.3.6
thinc                                   8.2.5
threadpoolctl                           3.5.0
thundergbm                              0.3.17
tifffile                                2024.9.20
tigramite                               5.2.6.7
tiktoken                                0.8.0
tinycss2                                1.4.0
tinygrad                                0.10.0
tokenizers                              0.20.3
toml                                    0.10.2
toolz                                   0.12.1
torch                                   2.4.1
torch_cluster                           1.6.3
torch-geometric                         2.6.1
torch_scatter                           2.1.2
torch_sparse                            0.6.18
torch_spline_conv                       1.2.2
torchdata                               0.10.0
torchmetrics                            1.6.0
torchvision                             0.20.1
tornado                                 6.4.2
TPOT                                    0.12.2
tqdm                                    4.66.5
traitlets                               5.14.3
transformers                            4.46.3
treelite                                4.3.0
triad                                   0.9.8
triton                                  3.0.0
truststore                              0.8.0
tsdownsample                            0.1.3
tsfel                                   0.1.9
tsfresh                                 0.20.2
tslearn                                 0.6.3
tweepy                                  4.14.0
typeguard                               2.13.3
typer                                   0.9.4
typer-config                            1.4.2
types-python-dateutil                   2.9.0.20241206
typing_extensions                       4.12.2
typing-inspect                          0.9.0
tzdata                                  2024.2
uc-micro-py                             1.0.3
ucx-py-cu12                             0.39.2
ucxx-cu12                               0.39.1
umap-learn                              0.5.7
uni2ts                                  1.2.0
update-checker                          0.18.0
uri-template                            1.3.0
uritemplate                             4.1.1
urllib3                                 2.2.3
utilsforecast                           0.2.10
wasabi                                  1.1.3
wcwidth                                 0.2.13
weasel                                  0.4.1
webargs                                 8.6.0
webcolors                               24.11.1
webencodings                            0.5.1
websocket-client                        1.8.0
websockets                              14.1
Werkzeug                                3.1.3
wheel                                   0.44.0
widgetsnbextension                      4.0.13
window_ops                              0.0.15
woodwork                                0.31.0
wordcloud                               1.9.4
wrapt                                   1.14.1
wurlitzer                               3.1.1
x-transformers                          1.42.24
xarray                                  2024.11.0
xarray-einstats                         0.8.0
xgboost                                 2.1.3
xlrd                                    2.0.1
XlsxWriter                              3.2.0
xxhash                                  3.5.0
xyzservices                             2024.9.0
yarl                                    1.18.3
ydf                                     0.9.0
yellowbrick                             1.5
yfinance                                0.2.50
zict                                    3.0.0
zipp                                    3.21.0
zope.event                              5.0
zope.interface                          7.2
zstandard                               0.23.0    

Autogluon Environment

This environment is only available for Python.

The Autogluon environment provides the following libraries:

absl-py                                 2.1.0
accelerate                              0.34.2
adagio                                  0.2.6
aesara                                  2.9.4
aiohappyeyeballs                        2.4.4
aiohttp                                 3.11.10
aiohttp-cors                            0.7.0
aiosignal                               1.3.1
aiosqlite                               0.20.0
alembic                                 1.14.0
alibi-detect                            0.12.0
alphalens-reloaded                      0.4.5
altair                                  5.5.0
anaconda-anon-usage                     0.4.4
annotated-types                         0.7.0
antlr4-python3-runtime                  4.9.3
anyio                                   4.7.0
aplr                                    10.8.0
appdirs                                 1.4.4
apricot-select                          0.6.1
arch                                    7.2.0
archspec                                0.2.3
argon2-cffi                             23.1.0
argon2-cffi-bindings                    21.2.0
arrow                                   1.3.0
arviz                                   0.20.0
astropy                                 7.0.0
astropy-iers-data                       0.2024.12.9.0.36.21
asttokens                               3.0.0
astunparse                              1.6.3
async-lru                               2.0.4
attrs                                   24.2.0
Authlib                                 1.3.2
autogluon                               1.2
autogluon.common                        1.2
autogluon.core                          1.2
autogluon.features                      1.2
autogluon.multimodal                    1.2
autogluon.tabular                       1.2
autogluon.timeseries                    1.2
autograd                                1.7.0
autograd-gamma                          0.5.0
autokeras                               2.0.0
autoray                                 0.7.0
ax-platform                             0.4.3
babel                                   2.16.0
bayesian-optimization                   2.0.0
beautifulsoup4                          4.12.3
bleach                                  6.2.0
blinker                                 1.9.0
blis                                    0.7.11
blosc2                                  2.7.1
bokeh                                   3.6.2
boltons                                 23.0.0
boto3                                   1.35.86
botocore                                1.35.86
botorch                                 0.12.0
Bottleneck                              1.4.2
Brotli                                  1.0.9
cachetools                              5.5.0
captum                                  0.7.0
catalogue                               2.0.10
catboost                                1.2.7
category-encoders                       2.6.4
causal-conv1d                           1.5.0.post8
certifi                                 2024.8.30
cesium                                  0.12.1
cffi                                    1.17.1
chardet                                 5.2.0
charset-normalizer                      3.3.2
check-shapes                            1.1.1
chronos-forecasting                     1.4.1
clarabel                                0.9.0
click                                   8.1.7
clikit                                  0.6.2
cloudpathlib                            0.20.0
cloudpickle                             3.1.0
cmdstanpy                               1.2.4
colorama                                0.4.6
colorcet                                3.1.0
colorful                                0.5.6
colorlog                                6.9.0
colorlover                              0.3.0
colour                                  0.1.5
comm                                    0.2.2
conda                                   24.9.2
conda-content-trust                     0.2.0
conda-libmamba-solver                   24.9.0
conda-package-handling                  2.3.0
conda_package_streaming                 0.10.0
confection                              0.1.5
cons                                    0.4.6
contourpy                               1.3.1
control                                 0.10.1
copulae                                 0.7.9
copulas                                 0.12.0
coreforecast                            0.0.12
cramjam                                 2.9.0
crashtest                               0.3.1
creme                                   0.6.1
cryptography                            43.0.0
cucim-cu12                              24.8.0
cuda-python                             12.6.2.post1
cudf-cu12                               24.8.3
cufflinks                               0.17.3
cugraph-cu12                            24.8.0
cuml-cu12                               24.8.0
cuproj-cu12                             24.8.0
cupy-cuda12x                            13.3.0
cuspatial-cu12                          24.8.0
cuvs-cu12                               24.8.0
cuxfilter-cu12                          24.8.0
cvxopt                                  1.3.2
cvxportfolio                            1.4.0
cvxpy                                   1.6.0
cycler                                  0.12.1
cymem                                   2.0.10
Cython                                  3.0.11
darts                                   0.31.0
dash                                    2.9.3
dash-core-components                    2.0.0
dash_cytoscape                          1.0.2
dash-html-components                    2.0.0
dash-table                              5.0.0
dask                                    2024.7.1
dask-cuda                               24.8.2
dask-cudf-cu12                          24.8.3
dask-expr                               1.1.9
databricks-sdk                          0.38.0
dataclasses-json                        0.6.7
datasets                                2.21.0
datashader                              0.16.3
deap                                    1.4.1
debugpy                                 1.8.9
decorator                               5.1.1
deepmerge                               2.0
defusedxml                              0.7.1
Deprecated                              1.2.15
deprecation                             2.1.0
dgl                                     2.1.0
dill                                    0.3.8
dimod                                   0.12.17
dirtyjson                               1.0.8
diskcache                               5.6.3
distlib                                 0.3.9
distributed                             2024.7.1
distributed-ucxx-cu12                   0.39.1
distro                                  1.9.0
dm-tree                                 0.1.8
docker                                  7.1.0
docutils                                0.21.2
DoubleML                                0.9.0
dropstackframe                          0.1.1
dtreeviz                                2.2.2
dtw-python                              1.5.3
dwave-cloud-client                      0.13.1
dwave-drivers                           0.4.4
dwave-gate                              0.3.2
dwave-greedy                            0.3.0
dwave-hybrid                            0.6.12
dwave-inspector                         0.5.1
dwave-inspectorapp                      0.3.3
dwave-neal                              0.6.0
dwave_networkx                          0.8.15
dwave-ocean-sdk                         8.0.1
dwave-optimization                      0.3.0
dwave-preprocessing                     0.6.6
dwave-samplers                          1.3.0
dwave-system                            1.26.0
dwave-tabu                              0.5.0
dwavebinarycsp                          0.3.0
ecos                                    2.0.14
einops                                  0.8.0
einx                                    0.3.0
EMD-signal                              1.6.4
empyrical-reloaded                      0.5.11
en-core-web-md                          3.7.1
en-core-web-sm                          3.7.1
et_xmlfile                              2.0.0
etuples                                 0.3.9
evaluate                                0.4.3
exchange_calendars                      4.6
executing                               2.1.0
faiss-cpu                               1.9.0.post1
Farama-Notifications                    0.0.4
fastai                                  2.7.18
fastai2                                 0.0.30
fastcore                                1.7.26
fastdownload                            0.0.7
fasteners                               0.19
fastjsonschema                          2.21.1
fastparquet                             2024.11.0
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gpytorch                                1.13
graphene                                3.4.3
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graphviz                                0.20.3
greenlet                                3.1.1
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gym                                     0.26.2
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h11                                     0.14.0
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h5py                                    3.12.1
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ibm-cloud-sdk-core                      3.22.0
ibm-platform-services                   0.59.0
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jax                                     0.4.35
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jsonschema                              4.21.1
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llama-index-indices-managed-llama-cloud 0.6.3
llama-index-legacy                      0.9.48.post4
llama-index-llms-openai                 0.3.3
llama-index-multi-modal-llms-openai     0.3.0
llama-index-program-openai              0.3.1
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Mako                                    1.3.8
mamba-ssm                               2.2.4
MAPIE                                   0.9.1
marisa-trie                             1.2.1
Markdown                                3.7
markdown-it-py                          3.0.0
MarkupSafe                              3.0.2
marshmallow                             3.23.1
matplotlib                              3.7.5
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memray                                  1.15.0
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mlflow-skinny                           2.18.0
mlforecast                              0.13.4
mljar-scikit-plot                       0.3.12
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mlxtend                                 0.23.3
mmh3                                    2.5.1
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mplfinance                              0.12.10b0
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murmurhash                              1.0.11
mypy-extensions                         1.0.0
namex                                   0.0.8
narwhals                                1.17.0
nbclient                                0.10.1
nbconvert                               7.16.4
nbformat                                5.10.4
ndindex                                 1.9.2
nest-asyncio                            1.6.0
networkx                                3.4.2
neural-tangents                         0.6.5
neuralprophet                           0.9.0
nfoursid                                1.0.1
ngboost                                 0.5.1
ninja                                   1.11.1.2
nixtla                                  0.6.4
nlpaug                                  1.1.11
nltk                                    3.8.1
nolds                                   0.6.1
nose                                    1.3.7
notebook                                7.3.1
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numba                                   0.59.1
numerapi                                2.19.1
numexpr                                 2.10.2
numpy                                   1.26.4
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nvidia-cuda-cupti-cu12                  12.4.127
nvidia-cuda-nvrtc-cu12                  12.4.127
nvidia-cuda-runtime-cu12                12.4.127
nvidia-cudnn-cu12                       9.1.0.70
nvidia-cufft-cu12                       11.2.1.3
nvidia-curand-cu12                      10.3.5.147
nvidia-cusolver-cu12                    11.6.1.9
nvidia-cusparse-cu12                    12.3.1.170
nvidia-ml-py3                           7.352.0
nvidia-nccl-cu12                        2.21.5
nvidia-nvjitlink-cu12                   12.4.127
nvidia-nvtx-cu12                        12.4.127
nvtx                                    0.2.10
nx-cugraph-cu12                         24.8.0
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openai                                  1.57.0
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opencv-contrib-python-headless          4.10.0.84
opencv-python                           4.10.0.84
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optuna                                  4.1.0
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orjson                                  3.10.12
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pbr                                     6.1.0
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peewee                                  3.17.3
peft                                    0.13.2
penaltymodel                            1.1.0
PennyLane                               0.39.0
PennyLane_Lightning                     0.39.0
PennyLane-qiskit                        0.36.0
persim                                  0.3.7
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pgmpy                                   0.1.26
pillow                                  10.4.0
pingouin                                0.5.5
pip                                     24.0
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plotly                                  5.24.1
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ply                                     3.11
pmdarima                                2.0.4
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protobuf                                5.29.1
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PuLP                                    2.9.0
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py-heat                                 0.0.6
py-heat-magic                           0.0.2
py_lets_be_rational                     1.0.1
py-spy                                  0.4.0
py_vollib                               1.0.1
py4j                                    0.10.9.7
pyaml                                   24.9.0
pyarrow                                 16.1.0
pyasn1                                  0.6.1
pyasn1_modules                          0.4.1
pybind11                                2.13.6
pycaret                                 3.3.2
pycosat                                 0.6.6
pycparser                               2.21
pycryptodome                            3.21.0
pyct                                    0.5.0
pydantic                                2.9.2
pydantic_core                           2.23.4
PyDMD                                   2024.12.1
pyerfa                                  2.0.1.5
pyfolio-reloaded                        0.9.8
Pygments                                2.18.0
PyJWT                                   2.10.1
pykalman                                0.9.7
pylev                                   1.4.0
pylibcugraph-cu12                       24.8.0
pylibraft-cu12                          24.8.1
pyluach                                 2.2.0
pymannkendall                           1.4.3
pymc                                    5.19.0
pymdptoolbox                            4.0b3
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pynvjitlink-cu12                        0.4.0
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pyod                                    2.0.2
pyogrio                                 0.10.0
Pyomo                                   6.8.2
pyparsing                               3.2.0
pypdf                                   5.1.0
pyportfolioopt                          1.5.6
pyproj                                  3.7.0
PyQt6                                   6.7.1
PyQt6-Qt6                               6.7.3
PyQt6_sip                               13.9.0
pyrb                                    1.0.1
pyre-extensions                         0.0.32
pyro-api                                0.1.2
pyro-ppl                                1.9.1
pysimdjson                              6.0.2
PySocks                                 1.7.1
pyspnego                                0.11.2
pystan                                  3.10.0
pytensor                                2.26.4
pytesseract                             0.3.10
pytest                                  8.3.4
pytest-runner                           6.0.1
python-dateutil                         2.9.0.post0
python-json-logger                      2.0.7
python-statemachine                     2.5.0
pytorch-forecasting                     1.2.0
pytorch-ignite                          0.5.1
pytorch-lightning                       2.4.0
pytorch-metric-learning                 2.3.0
pytorch-tabnet                          4.1.0
pytz                                    2024.2
pyvinecopulib                           0.6.5
pyviz_comms                             3.0.3
PyWavelets                              1.7.0
PyYAML                                  6.0.2
pyzmq                                   26.2.0
qdldl                                   0.1.7.post4
qiskit                                  1.2.4
qiskit-aer                              0.15.1
qiskit-ibm-provider                     0.11.0
qiskit-ibm-runtime                      0.34.0
quadprog                                0.1.13
quantecon                               0.7.2
QuantLib                                1.36
QuantStats                              0.0.64
raft-dask-cu12                          24.8.1
rapids-dask-dependency                  24.8.0
rauth                                   0.7.3
ray                                     2.39.0
Rbeast                                  0.1.23
referencing                             0.35.1
regex                                   2024.11.6
requests                                2.32.3
requests_ntlm                           1.3.0
requests-oauthlib                       1.3.1
requests-toolbelt                       1.0.0
rfc3339-validator                       0.1.4
rfc3986-validator                       0.1.1
rich                                    13.9.4
ripser                                  0.6.10
Riskfolio-Lib                           6.1.1
riskparityportfolio                     0.6.0
river                                   0.21.0
rmm-cu12                                24.8.2
rpds-py                                 0.22.3
rsa                                     4.9
ruamel.yaml                             0.18.6
ruamel.yaml.clib                        0.2.8
ruptures                                1.1.9
rustworkx                               0.15.1
s3transfer                              0.10.4
safetensors                             0.4.5
SALib                                   1.5.1
schemdraw                               0.15
scikeras                                0.13.0
scikit-base                             0.7.8
scikit-image                            0.22.0
scikit-learn                            1.4.2
scikit-learn-extra                      0.3.0
scikit-optimize                         0.10.2
scikit-plot                             0.3.7
scikit-tda                              1.1.1
scipy                                   1.11.4
scs                                     3.2.7
sdeint                                  0.3.0
seaborn                                 0.13.2
Send2Trash                              1.8.3
sentence-transformers                   3.3.1
sentencepiece                           0.2.0
seqeval                                 1.2.2
setuptools                              65.5.0
setuptools-scm                          8.1.0
shap                                    0.46.0
shapely                                 2.0.6
Shimmy                                  2.0.0
simpervisor                             1.0.0
simplejson                              3.19.3
simpy                                   4.1.1
six                                     1.17.0
sklearn-json                            0.1.0
sktime                                  0.26.0
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smart-open                              7.0.5
smmap                                   5.0.1
sniffio                                 1.3.1
sortedcontainers                        2.4.0
soupsieve                               2.6
spacy                                   3.7.5
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spacy-loggers                           1.0.5
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stack-data                              0.6.3
stanio                                  0.5.1
statsforecast                           1.7.8
statsmodels                             0.14.4
stevedore                               5.4.0
stochastic                              0.6.0
stockstats                              0.6.2
stopit                                  1.1.2
striprtf                                0.0.26
stumpy                                  1.13.0
symengine                               0.13.0
sympy                                   1.13.1
ta                                      0.11.0
ta-lib                                  0.5.1
tables                                  3.10.1
tabulate                                0.8.10
tadasets                                0.2.1
tbats                                   1.1.3
tblib                                   3.0.0
tenacity                                8.5.0
tensorboard                             2.18.0
tensorboard-data-server                 0.7.2
tensorboardX                            2.6.2.2
tensorflow                              2.18.0
tensorflow-addons                       0.23.0
tensorflow_decision_forests             1.11.0
tensorflow-io-gcs-filesystem            0.37.1
tensorflow-probability                  0.25.0
tensorflow-text                         2.18.0
tensorly                                0.9.0
tensorrt                                10.7.0
tensorrt_cu12                           10.7.0
tensorrt-cu12-bindings                  10.7.0
tensorrt-cu12-libs                      10.7.0
tensortrade                             1.0.3
termcolor                               2.5.0
terminado                               0.18.1
text-unidecode                          1.3
textual                                 1.0.0
tf_keras                                2.18.0
tf2jax                                  0.3.6
thinc                                   8.2.5
threadpoolctl                           3.5.0
thundergbm                              0.3.17
tifffile                                2024.9.20
tigramite                               5.2.6.7
tiktoken                                0.8.0
timm                                    1.0.3
tinycss2                                1.4.0
tinygrad                                0.10.0
tokenizers                              0.20.3
toml                                    0.10.2
toolz                                   0.12.1
torch                                   2.5.1
torch_cluster                           1.6.3
torch-geometric                         2.6.1
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torch_sparse                            0.6.18
torch_spline_conv                       1.2.2
torchdata                               0.10.0
torchmetrics                            1.2.1
torchvision                             0.20.1
tornado                                 6.4.2
TPOT                                    0.12.2
tqdm                                    4.66.5
traitlets                               5.14.3
transformers                            4.46.3
treelite                                4.3.0
triad                                   0.9.8
triton                                  3.1.0
truststore                              0.8.0
tsdownsample                            0.1.3
tsfel                                   0.1.9
tsfresh                                 0.20.2
tslearn                                 0.6.3
tweepy                                  4.14.0
typeguard                               2.13.3
typer                                   0.9.4
typer-config                            1.4.2
types-python-dateutil                   2.9.0.20241206
typing_extensions                       4.12.2
typing-inspect                          0.9.0
tzdata                                  2024.2
uc-micro-py                             1.0.3
ucx-py-cu12                             0.39.2
ucxx-cu12                               0.39.1
umap-learn                              0.5.7
update-checker                          0.18.0
uri-template                            1.3.0
uritemplate                             4.1.1
urllib3                                 2.2.3
utilsforecast                           0.2.4
virtualenv                              20.28.0
wasabi                                  1.1.3
wcwidth                                 0.2.13
weasel                                  0.4.1
webargs                                 8.6.0
webcolors                               24.11.1
webencodings                            0.5.1
websocket-client                        1.8.0
websockets                              14.1
Werkzeug                                3.1.3
wheel                                   0.44.0
widgetsnbextension                      4.0.13
window_ops                              0.0.15
woodwork                                0.31.0
wordcloud                               1.9.4
wrapt                                   1.16.0
wurlitzer                               3.1.1
x-transformers                          1.42.24
xarray                                  2024.11.0
xarray-einstats                         0.8.0
xgboost                                 2.1.3
xlrd                                    2.0.1
XlsxWriter                              3.2.0
xxhash                                  3.5.0
xyzservices                             2024.9.0
yarl                                    1.18.3
ydf                                     0.9.0
yellowbrick                             1.5
yfinance                                0.2.50
zict                                    3.0.0
zipp                                    3.21.0
zope.event                              5.0
zope.interface                          7.2
zstandard                               0.23.0      

Request New Libraries

To request a new library, contact us . We will add the library to the queue for review and deployment. Since the libraries run on our servers, we need to ensure they are secure and won't cause harm. The process of adding new libraries takes 2-4 weeks to complete. View the list of libraries currently under review on the Issues list of the Lean GitHub repository .

Projects > LEAN Engine Versions

Introduction

The latest master branch on the LEAN GitHub repository is the default engine branch that runs the backtesting, research, and live trading nodes in QuantConnect Cloud. The latest version of LEAN is generally the safest as it includes all bug fixes.

Trading Firm or Institution tier users concerned for stability can elect to use older or custom versions of LEAN in the IDE. These are powered by the QuantConnect/LEAN Github Branches . We use a continuous deployment process to ship custom branches to production for trading. To create a custom version of LEAN, make a pull request to LEAN which will be reviewed by our team.

Change Branches

Follow these steps to change the LEAN engine branch that runs your backtests and live trading algorithms:

1. Open a project .
2. In the Project panel, click the LEAN Engine field and then click a branch from the drop-down menu.
3. (Optional) Click About Version to display the branch description.
4. If you want to always use the master branch, select the Always use Master Branch check box.
5. Click Select .

Changing the Lean engine branch only affects the current project. If you create a new project , the new project will use the master branch by default.

Request New Branches

Before starting a pull-request to create a new branch, contact us to discuss the goals of the project.

Research

Research > Getting Started

Introduction

The Research Environment is a Jupyter notebook -based, interactive commandline environment where you can access our data through the QuantBook class. The environment supports both Python and C#. If you use Python, you can import code from the code files in your project into the Research Environment to aid development.

Before you run backtests, we recommend testing your hypothesis in the Research Environment. It's easier to perform data analysis and produce plots in the Research Environment than in a backtest. The Research Environment also supports debugging for Python projects.

Before backtesting or live trading with machine learning models, you may find it beneficial to train them in the Research Environment, save them in the Object Store, and then load them from the Object Store into the backtesting and live trading environment

In the Research Environment, you can also use the QuantConnect API to import your backtest results for further analysis.

Example

The following snippet demonstrates how to use the Research Environment to plot the price and Bollinger Bands of the S&P 500 index ETF, SPY:

The following snippet demonstrates how to use the Research Environment to print the price of the S&P 500 index ETF, SPY:

// Load the required assembly files and data types in a separate cell.
#load "../Initialize.csx"

# Create a QuantBook
qb = QuantBook()

# Add an asset.
symbol = qb.add_equity("SPY").symbol

# Request some historical data.
history = qb.history(symbol, 360, Resolution.DAILY)

# Calculate the Bollinger Bands.
bbdf = qb.indicator(BollingerBands(30, 2), symbol, 360, Resolution.DAILY)

# Plot the data
bbdf[['price', 'lowerband', 'middleband', 'upperband']].plot();

#load "../QuantConnect.csx"
using QuantConnect;
using QuantConnect.Data;
using QuantConnect.Algorithm;
using QuantConnect.Research;

// Create a QuantBook
var qb = new QuantBook();

// Create a security subscription
var symbol = qb.AddEquity("SPY").Symbol;

// Request some historical data
var history = qb.History(symbol, 70, Resolution.Daily);

foreach (var tradeBar in history)
{
    Console.WriteLine($"{tradeBar.EndTime} :: {tradeBar.ToString()}");
}

Open Notebooks

Each new project you create contains a notebook file by default. Follow these steps to open the notebook:

1. Open the project .
2. In the right navigation menu, click the Explorer icon.
3. In the Explorer panel, expand the Workspace (Workspace) section.
4. Click the Research.ipynb research.ipynb file.

When you open a notebook, it automatically tries to connect to the correct Jupyter server and select the correct kernel, which can take up to one minute. If the top-right corner of the notebook displays a base (Python x.x.x) button, wait for the button to change to Foundation-C#-Default Foundation-Py-Default before you run the cells. If you run cells before the notebook connects to the server and kernel, you may get the following error message:

Run Notebook Cells

Notebooks are a collection of cells where you can write code snippets or MarkDown. To execute a cell, press Shift+Enter .

The following describes some helpful keyboard shortcuts to speed up your research:

There is a 15 minute timeout period before the cells becomes unresponsive. If this occurs, restart the notebook to be able to run cells again.

Stop Nodes

You need stop node permissions to stop research nodes in the cloud.

Follow these steps to stop a research node:

1. Open the project .
2. In the right navigation menu, click the Resources icon.
3. Click the stop button next to the research node you want to stop.

Add Notebooks

Follow these steps to add notebook files to a project:

1. Open the project .
2. In the right navigation menu, click the Explorer icon.
3. In the Explorer panel, expand the Workspace (Workspace) section.
4. Click the New File icon.
5. Enter fileName .ipynb .
6. Press Enter .

Rename Notebooks

Follow these steps to rename a notebook in a project:

1. Open the project .
2. In the right navigation menu, click the Explorer icon.
3. In the Explorer panel, right-click the notebook you want to rename and then click Rename .
4. Enter the new name and then press Enter .

The following directory names are reserved: .ipynb_checkpoints , .idea , .vscode , __pycache__ , bin , obj , backtests , live , optimizations , storage , and report .

Delete Notebooks

Follow these steps to delete a notebook in a project:

1. Open the project .
2. In the right navigation menu, click the Explorer icon.
3. In the Explorer panel, right-click the notebook you want to delete and then click Delete Permanently .
4. Click Delete .

Learn Jupyter

The following table lists some helpful resources to learn Jupyter:

Research > Deployment

Introduction

This page is an introduction to the Research Environment for the Algorithm Lab. For more comprehensive information on using research notebooks, see the Research Environment documentation product.

Resources

Research nodes enable you to spin up an interactive, command-line, Jupyter Research Environment . Several models of research nodes are available. More powerful research nodes allow you to handle more data and run faster computations in your notebooks. The following table shows the specifications of the research node models:

Refer to the Pricing page to see the price of each research node model. You get one free R1-4 research node in your first organization, but the node is replaced when you subscribe to a paid research node in the organization.

To view the status of all of your organization's nodes, see the Resources panel of the IDE. When you launch the Research Environment, it uses the best-performing resource by default, but you can select a specific resource to use .

The CPU nodes are available on a fair usage basis. The GPU nodes can be shared with a maximum of three members. Depending on the server load, you may use all of the GPU's processing power.

Sharing

The Research Environment does not currently support simultaneous coding between two peers. You can view the notebook of your colleague as they edit it, but you can't both edit the notebook at the same time. To enable your team members to see your notebook, add them to the project .

Sharing your notebooks is helpful when publishing your findings online, getting feedback on your research process, and explaining your methodology to others. To share a notebook with members not in your organization, run a backtest , generate a link to share the backtest , and then give the backtest link to the other person. They will be able to clone the project and launch the Research Environment using the notebook files.

Historical Data

On our platform, you can access historical data for all of the asset classes that we support. We have datasets for Equities, Options, Futures, Crypto, Forex, CFD, Indices, and alternative data. View the Dataset Market to see all of the datasets that we support. To import custom data, see Custom Data .

Look-Ahead Bias

Look-ahead bias occurs when an algorithm makes decisions using data that would not have yet been available. As you work in the Research Environment, take measures to reduce look-ahead bias in your workflow. If look-ahead bias seeps into your research, you may find profitable results on historical data, however, you will not be able to apply your findings in a live trading algorithm because the data does not exist in real-time.

An example of look-ahead bias is using the closing price to make trading decisions and filling your orders at the same closing price. This can happen in the Research Environment because all of the data is available at the same time. This type of look-ahead bias cannot occur when backtesting with Lean because the data is fed into your algorithm in discrete slices of time and an order is filled using a slice that occurs after the order was placed.

A second example of look-ahead bias occurs when researchers test a model on the same dataset that was used to train the model. In this situation, the model is trained using data that would not have been available when the model was created in practice. A third example of look-ahead bias is when researchers select a universe to apply their trading strategy to based on the past performance of the universe.

Backtesting

Backtesting > Getting Started

Introduction

Backtesting is the process of simulating a trading algorithm on historical data. By running a backtest, you can measure how the algorithm would have performed in the past. Although past performance doesn't guarantee future results, an algorithm that has a proven track record can provide investors with more confidence when deploying to live trading than an algorithm that hasn't performed favorably in the past. Use the QuantConnect platform to run your backtests because we have institutional-grade datasets, an open-source backtesting engine that's constantly being improved, cloud servers to execute the backtests, and the backtesting hardware is maintained 24/7 by QuantConnect engineers.

Run Backtests

To run a backtest, open a project and then click the Backtest icon. If the project successfully builds, "Received backtest backtestName request" displays. If the backtest successfully launches, the IDE displays the backtest results page in a new tab. If the backtest fails to launch due to coding errors, the new tab displays the error. As the backtest executes, you can refresh or close the IDE without interfering with the backtest because it runs on our cloud servers. The backtest runs up to the start of the out-of-sample hold out period .

View All Backtests

Follow these steps to view all of the backtests of a project:

1. Open the project that contains the backtests you want to view.
2. In the top-right corner of the IDE, click the Backtest Results icon.
   

A table containing all of the backtest results for the project is displayed. If there is a play icon to the left of the name, it's a backtest result . If there is a fast-forward icon next to the name, it's an optimization result .


3. (Optional) In the top-right corner, select the Show field and then select one of the options from the drop-down menu to filter the table by backtest or optimization results.
4. (Optional) In the bottom-right corner, click the Hide Error check box to remove backtest and optimization results from the table that had a runtime error.
5. (Optional) Use the pagination tools at the bottom to change the page.
6. (Optional) Click a column name to sort the table by that column.
7. Click a row in the table to open the results page of that backtest or optimization.

Rename Backtests

We give an arbitrary name (for example, "Smooth Apricot Chicken") to your backtest result files, but you can follow these steps to rename them:

1. Open the backtest history of the project.
2. Hover over the backtest you want to rename and then click the pencil icon that appears.

3. Enter the new backtest name and then click OK .

To programmatically set the backtest name, call the SetName set_name method.

SetName("Backtest Name");

self.set_name("Backtest Name")

For more information, see Set Name and Tags .

Out of Sample Period

To reduce the chance of overfitting, organization managers can enforce all backtests must end a certain number of months before the current date. For example, if you set a one year out-of-sample period, the researchers on your team will not be able to use the most recent year of data in their backtests. A out-of-sample period is helpful because it leaves you a period to test your model after your done the development stage. Follow these steps to change the backtest out-of-sample period:

1. Open the organization homepage .
2. Scroll down to the Backtesting Out of Sample Period section.
3. Adjust the out-of-sample period duration or click on "No Holdout Period".

On-Premise Backtests

For information about on-premise backtests with Local Platform , see Getting Started .

Get Backtest Id

To get the backtest Id, see the first line of the log file . An example backtest Id is 8b16cec0c44f75188d82f9eadb310e17.

Share Backtests

The backtest results page enables you to share your backtest results. You need to make a backtest public in order to share it. To make a backtest public, in the Share Results section of the backtest results page, click Make Public . Once you make a backtest public, click Backtest URL to copy a link to the backtest result or click Embed Code to copy an iframe HTML element you can embed on a website.

The following widget is an example of an embedded backtest result:

To attach the embedded backtest result to a forum discussion, see Create Discussions or Post Comments .

After you've made your backtest results public, the results are always stored and anyone with the link can access the results. To make your backtest results private again, click Make Private .

Backtesting > Research Guide

Introduction

QuantConnect aims to teach and inspire our community to create high-performing algorithmic trading strategies. We measure our success by the profits created by the community through their live trading. As such, we try to build the best quantitative research techniques possible into the product to encourage a robust research process.

Hypothesis-Driven Research

We recommend you develop an algorithmic trading strategy based on a central hypothesis. You should develop an algorithm hypothesis at the start of your research and spend the remaining time exploring how to test your theory. If you find yourself deviating from your core theory or introducing code that isn't based around that hypothesis, you should stop and go back to thesis development.

Wang et al. (2014) illustrate the danger of creating your hypothesis based on test results. In their research, they examined the earnings yield factor in the technology sector over time. During 1998-1999, before the tech bubble burst, the factor was unprofitable. If you saw the results and then decided to bet against the factor during 2000-2002, you would have lost a lot of money because the factor performed extremely well during that time.

Hypothesis development is somewhat of an art and requires creativity and great observation skills. It is one of the most powerful skills a quant can learn. We recommend that an algorithm hypothesis follow the pattern of cause and effect. Your aim should be to express your strategy in the following sentence:

A change in {cause} leads to an {effect}.

To search for inspiration, consider causes from your own experience, intuition, or the media. Generally, causes of financial market movements fall into the following categories:

• Human psychology
• Real-world events/fundamentals
• Invisible financial actions

Consider the following examples:

There are millions of potential alpha strategies to explore, each of them a candidate for an algorithm. Once you have chosen a strategy, we recommend exploring it for no more than 8-32 hours, depending on your coding ability.

Research Panel

We launched the Research Guide in 2019 to inform you about common quantitative research pitfalls. It displays a power gauge for the number of backtests performed, the number of parameters used, and the time invested in the strategy. These measures can give a ballpark estimate of the overfitting risk of the project. Generally, as a strategy becomes more overfit on historical data, it is less likely to perform well in live trading. These properties were selected based on the recommended best practices of the global quantitative research community.

Restricting Backtests

According to current research, the number of backtests performed on an idea should be limited to prevent overfitting. In theory, each backtest performed on an idea moves it one step closer to being overfitted as you are testing and selecting for strategies written into your code instead of being based on a central thesis. For more information, see the paper Probability of Backtest Overfitting (Bailey, Borwein, Jim Zho, & López de Prado, 2015).

QuantConnect does not restrict the number of backtests performed on a project, but we have implemented the counter as a guide for your reference. Your coding skills are a factor in how many backtests constitute overfitting, so if you are a new programmer you can increase these targets.

Reducing Strategy Parameters

With just a handful of parameters, it is possible to create an algorithm that perfectly models historical markets. Current research suggests keeping your parameter count to a minimum to decrease the risk of overfitting.

Limiting Research Time Invested

As you spend more time on one algorithm, research suggests you are more likely to be overfitting the strategy to the data. It is common to become attached to an idea and spend weeks or months to perform well in a backtest. Assuming you are a proficient coder who fully understands the QuantConnect API, we recommend no more than 16 hours of work per experiment. In theory, within two full working days, you should be able to test a single hypothesis thoroughly.

Parameter Detection

Using parameters is almost unavoidable, but a strategy trends toward being overfitted as more parameters get added or fine-tuned. Adding or optimizing parameters should only be done by a robust methodology such as walk-forward optimization . The parameter detection system is a general guide to inform you of how many parameters are present in the algorithm. It looks for criteria to warn that code is potentially a parameter. The following table shows the criteria for parameters:

The following table shows common expressions that are not parameters:

Overfitting

Overfitting occurs when you fine-tune the parameters of an algorithm to fit the detail and noise of backtesting data to the extent that it negatively impacts the performance of the algorithm on new data. The problem is that the parameters don't necessarily apply to new data and thus negatively impact the algorithm's ability to generalize and trade well in all market conditions. The following table shows ways that overfitting can manifest itself:

An algorithm that is dynamic and generalizes to new data is more valuable to funds and individual investors. It is more likely to survive across different market conditions and apply to new asset classes and markets.

Out of Sample Period

To reduce the chance of overfitting, organization managers can enforce all backtests must end a certain number of months before the current date. For example, if you set a one year out-of-sample period, the researchers on your team will not be able to use the most recent year of data in their backtests. A out-of-sample period is helpful because it leaves you a period to test your model after your done the development stage. Follow these steps to change the backtest out-of-sample period:

1. Open the organization homepage .
2. Scroll down to the Backtesting Out of Sample Period section.
3. Adjust the out-of-sample period duration or click on "No Holdout Period".

Backtesting > Deployment

Introduction

Deploy a backtest to simulate your trading algorithm on our cloud servers. Since the same Lean engine is used to run backtests and live trading algorithms, it's easy to transition from backtesting to live trading once you are satisfied with the historical performance of your algorithm. If you find any issues with Lean, the historical data, or the website when running backtests, we'll resolve the issue.

Nodes

Backtesting nodes enable you to run backtests. The more backtesting nodes your organization has, the more concurrent backtests that you can run. Several models of backtesting nodes are available. Backtesting nodes that are more powerful can run faster backtests and backtest nodes with more RAM can handle more memory-intensive operations like training machine learning models, processing Options data, and managing large universes. The following table shows the specifications of the backtesting node models:

Refer to the Pricing page to see the price of each backtesting node model. You get one free B-MICRO backtesting node in your first organization. This node incurs a 20-second delay when you launch backtests, but the delay is removed and the node is replaced when upgrade your organization to a paid tier and add a new backtesting node .

To view the status of all of your organization's nodes, see the Resources panel of the IDE. When you run a backtest, it uses the best-performing resource by default, but you can select a specific resource to use .

The CPU nodes are available on a fair usage basis while the GPU nodes can be shared with a maximum of three members. Depending on the server load, you may use all of the GPU's processing power. GPU nodes perform best on repetitive and highly-parallel tasks like training machine learning models. It takes time to transfer the data to the GPU for computation, so if your algorithm doesn't train machine learning models, the extra time it takes to transfer the data can make it appear that GPU nodes run slower than CPU nodes.

Concurrent Backtesting

Concurrent backtesting is the process of running multiple backtests at the same time within a single organization. Concurrent backtesting speeds up your strategy development because you don't have to wait while a single backtest finishes executing. You need multiple backtesting nodes in your organization to run concurrent backtests. The more backtesting nodes that your organization has, the more concurrent backtests you can execute. If you are trying to fine-tune your parameters , consider running a parameter optimization . If you run a parameter optimization job, you can run multiple backtests concurrently without having multiple backtest nodes.

The number of backtesting nodes that you can have in your organization depends on the tier of your organization. The following table shows the backtesting node quotas:

Logs

The amount of logs you can generate per backtest and per day depends on the tier of your organization. The following table shows the amount of logs you can generate on each tier:

To check the log storage space you have remaining, log in to the Algorithm Lab and then, in the left navigation bar, click Organization > Resources .

Orders

The number of orders you can place in a single backtest depends on the tier of your organization. The following table shows the number of orders you can place on each tier:

Security

Your code is stored in a database, isolated from the internet. When the code leaves the database, it is compiled and obfuscated before being deployed to the cloud. If the cloud servers were compromised, this process makes it difficult to read your strategy.

As we've seen over recent years, there can never be any guarantee of security with online websites. However, we deploy all modern and common security procedures. We deploy nightly software updates to keep the server up to date with the latest security patches. We also use SSH key login to avoid reliance on passwords. Internally, we use processes to ensure only a handful of people have access to the database and we always restrict logins to never use root credentials.

See our Security and IP documentation for more information.

Build Projects

If the compiler finds errors during the build process, the IDE highlights the lines of code that caused the errors in red. Your projects will automatically build after each keystroke. To manually build a project, open the project and then click the Build icon. If the build process is unresponsive, try refreshing the page and building again. If the build process continues to be unresponsive, check our Status page or contact us .

Run Backtests

To run a backtest, open a project and then click the Backtest icon. If the project successfully builds, "Received backtest backtestName request" displays. If the backtest successfully launches, the IDE displays the backtest results page in a new tab. If the backtest fails to launch due to coding errors, the new tab displays the error. As the backtest executes, you can refresh or close the IDE without interfering with the backtest because it runs on our cloud servers. The backtest runs up to the start of the out-of-sample hold out period .

Stop Backtests

To stop a running backtest, open the Resources panel , and then click the stop icon next to the backtest node. You can stop nodes that you are using, but you need stop node permissions to stop nodes other members are using.

Runtime Quota

The maximum amount of time a backtest can run for is 12 hours. The runtime depends on the amount of data, the algorithm features and computations, and the backtest node type.

Backtesting > Results

Introduction

The backtest results page shows your algorithm's performance. Review the results page to see how your algorithm has performed during the backtest and to investigate how you might improve your algorithm before live trading.

View Backtest Results

The backtest results page automatically displays when you deploy a backtest . The backtest results page presents the equity curve, trades, logs, performance statistics, and much more information.

The content in the backtest results page updates as your backtest executes. You can close or refresh the window without interrupting the backtest because the backtesting node processes on our servers. If you close the page, to open the page again, view all of the project's backtests . Unless you explicitly make the backtest public, only you can view its results. If you delete a backtest result or you are inactive for 12 months, we archive your backtest results.

Runtime Statistics

The banner at the top of the backtest results page displays the runtime statistics of your backtest.

The following table describes the default runtime statistics:

To download the runtime statistics data, see Download Results .

To add a custom runtime statistic, see Add Statistics .

Built-in Charts

The backtest results page displays a set of built-in charts to help you analyze the performance of your algorithm. The following table describes the charts displayed on the page:

To download the chart data, see Download Results .

Asset Plots

Asset plots display the trade prices of an asset and the following order events you have for the asset:

The following image shows an example asset plot for AAPL:

The order submission icons aren't visible by default.

View Plots

Follow these steps to open an asset plot:

1. Open the backtest results page.
2. Click the Orders tab.
3. Click the Asset Plot icon that's next to the asset Symbol in the Orders table.

Tool Tips

When you hover over one of the order events in the table, the asset plot highlights the order event, displays the asset price at the time of the event, and displays the tag associated with the event. Consider adding helpful tags to each order event to help with debugging your algorithm. For example, when you cancel an order, you can add a tag that explains the reason for cancelling it.

Adjust the Display Period

The resolution of the asset price time series in the plot doesn't necessarily match the resolution you set when you subscribed to the asset in your algorithm. If you are displaying the entire price series, the series usually displays the daily closing price. However, when you zoom in, the chart will adjust its display period and may use higher resolution data. To zoom in and out, perform either of the following actions:

• Click the 1m , 3m , 1y , or All period in the top-right corner of the chart.
• Click a point on the chart and drag your mouse horizontally to highlight a specific period of time in the chart.

If you have multiple order events in a single day and you zoom out on the chart so that it displays the daily closing prices, the plot aggregates the order event icons together as the price on that day.

Order Fill Prices

The plot displays fill order events at the actual fill price of your orders. The fill price is usually not equal to the asset price that displays because of the following reasons:

• Your order experiences slippage .
• If you use quote data, your order fills at the bid or ask price.
• The fill model may fill your order at the high or low price.

Custom Charts

The results page shows the custom charts that you create.

Supported Chart Types

We support the following types of charts:

If you use SeriesType.Candle and plot enough values, the plot displays candlesticks. However, the Plot plot method only accepts one numerical value per time step, so you can't plot candles that represent the open, high, low, and close values of each bar in your algorithm. The charting software automatically groups the data points you provide to create the candlesticks, so you can't control the period of time that each candlestick represents.

To create other types of charts, save the plot data in the Object Store and then load it into the Research Environment. In the Research Environment, you can create other types of charts with third-party charting packages .

Supported Markers

When you create scatter plots, you can set a marker symbol. We support the following marker symbols:

Chart Quotas

Intensive charting requires hundreds of megabytes of data, which is too much to stream online or display in a web browser. The number of series and the number of data points per series you can plot depends on your organization tier . The following table shows the quotas:

If you exceed the series quota, your algorithm stops executing and the following message displays:

Exceeded maximum chart series count, new series will be ignored. Limit is currently set at <quota>.

If you exceed the data points per series quota, the following message displays:

Exceeded maximum points per chart, data skipped

If your plotting needs exceed the preceding quotas, create the plots in the Research Environment instead .

Demonstration

For more information about creating custom charts, see Charting .

Adjust Charts

You can manipulate the charts displayed on the backtest results page.

Toggle Charts

To display and hide a chart on the backtest results page, in the Select Chart section, click the name of a chart.

Toggle Chart Series

To display and hide a series on a chart on the backtest results page, click the name of a series at the top of a chart.

Adjust the Display Period

To zoom in and out of a time series chart on the backtest results page, perform either of the following actions:

• Click the 1m , 3m , 1y , or All period in the top-right corner of the chart.
• Click a point on the chart and drag your mouse horizontally to highlight a specific period of time in the chart.

If you adjust the zoom on a chart, it affects all of the charts.

After you zoom in on a chart, slide the horizontal bar at the bottom of the chart to adjust the time frame that displays.

Resize Charts

To resize a chart on the backtest results page, hover over the bottom-right corner of the chart. When the resize cursor appears, hold the left mouse button and then drag to the desired size.

Move Charts

To move a chart on the backtest results page, click, hold, and drag the chart title.

Refresh Charts

Refreshing the charts on the backtest results page resets the zoom level on all the charts. If you refresh the charts while your algorithm is executing, only the data that was seen by the Lean engine after you refreshed the charts is displayed. To refresh the charts, in the Select Chart section, click the reset icon.

Key Statistics

The backtest results page displays many key statistics to help you analyze the performance of your algorithm.

Overall Statistics

The Overview tab on the backtest results page displays tables for Overall Statistics and Rolling Statistics. The Overall Statistics table displays the following statistics:

• Probabilistic Sharpe Ratio (PSR)
• Total Trades
• Average Loss
• Drawdown
• Net Profit
• Loss Rate
• Profit-Loss Ratio
• Beta
• Annual Variance
• Tracking Error
• Total Fees
• Lowest Capacity Asset
• Sharpe Ratio
• Average Win
• Compounding Annual Return
• Expectancy
• Win Rate
• Alpha
• Annual Standard Deviation
• Information Ratio
• Treynor Ratio
• Estimated Strategy Capacity

Some of the preceding statistics are sampled throughout the backtest to produce a time series of rolling statistics. The time series are displayed in the Rolling Statistics table.

To download the data from the Overall Statistics and Rolling Statistics tables, see Download Results .

Research Guide

For information about the Research Guide, see Research Guide .

Reports

Backtest reports provide a summary of your algorithm's performance during the backtest period. Follow these steps to generate one:

1. Open the backtest results page for which you want to generate a report.
2. Click the Report tab.
3. If the project doesn't have a description, enter one and then click Save .
4. Click Download Report .

The report may take a minute to generate.

5. If the IDE says that the report is being generated, repeat step 4.

Orders

The backtest results page displays the orders of your algorithm and you can download them to your local machine.

View in the GUI

To see the orders that your algorithm created, open the backtest results page and then click the Orders tab. If there are more than 10 orders, use the pagination tools at the bottom of the Orders Summary table to see all of the orders. Click on an individual order in the Orders Summary table to reveal all of the order events , which include:

• Submissions
• Fills
• Partial fills
• Updates
• Cancellations
• Option contract exercises and expiration

The timestamps in the Order Summary table are based in Eastern Time (ET).

Access the Order Summary CSV

To view the orders data in CSV format, open the backtest results page, click the Orders tab, and then click Download Orders . The content of the CSV file is the content displayed in the Orders Summary table when the table rows are collapsed. The timestamps in the CSV file are based in Coordinated Universal Time (UTC).

Access the Orders JSON

To view all of the content in the Orders Summary table, see Download Results .

Access in Jupyter Notebooks

To programmatically analyze orders, call the ReadBacktestOrders read_backtest_orders method or the /backtests/orders/read endpoint.

Insights

The backtest results page displays the insights of your algorithm and you can download them to your local machine.

View in the GUI

To see the insights your algorithm emit, open the backtest result page and then click the Insights tab. If there are more than 10 insights, use the pagination tools at the bottom of the Insights Summary table to see all of the insights. The timestamps in the Insights Summary table are based in Eastern Time (ET).

Download JSON

To view the insights in JSON format, open the backtest result page, click the Insights tab, and then click Download Insights . The timestamps in the CSV file are based in Coordinated Universal Time (UTC).

Logs

The backtest results page displays the logs of your backtest and you can download them to your local machine. The timestamps of the statements in the log file are based in your algorithm time zone .

View in the GUI

To see the log file that was created throughout a backtest, open the backtest result page and then click the Logs tab.

To filter the logs, enter a search string in the Filter logs field.

Download Log Files

To download the log file that was created throughout a backtest, follow these steps:

1. Open the backtest result page.
2. Click the Logs tab.
3. Click Download Logs .

Project Files

The backtest results page displays the project files used to run the backtest. To view the files, click the Code tab. By default, the main.py Main.cs file displays. To view other files in the project, click the file name and then select a different file from the drop-down menu.

Share Results

The backtest results page enables you to share your backtest results. You need to make a backtest public in order to share it. To make a backtest public, in the Share Results section of the backtest results page, click Make Public . Once you make a backtest public, click Backtest URL to copy a link to the backtest result or click Embed Code to copy an iframe HTML element you can embed on a website.

The following widget is an example of an embedded backtest result:

To attach the embedded backtest result to a forum discussion, see Create Discussions or Post Comments .

After you've made your backtest results public, the results are always stored and anyone with the link can access the results. To make your backtest results private again, click Make Private .

Download Results

You can download the following information from the backtest results page:

• Runtime statistics
• Charts
• The data in the Overview tab
• The data in the Orders tab

To download the preceding information, open the backtest results page, click the Overview tab, and then click Download Results . The timestamps in the file are based in Coordinated Universal Time (UTC).

The information is stored in JSON format. The following code converts the JSON result file into CSV format.

using System;
using System.Collections.Generic;
using System.Data;
using System.IO;
using Newtonsoft.Json;
using Newtonsoft.Json.Linq;

static DataTable CreateMultiIndexDataTable(JObject jsonObject)
{
    DataTable dataTable = new DataTable();
    dataTable.Columns.Add("Key Path");
    dataTable.Columns.Add("Value");

    // Flatten the JSON and populate the DataTable.
    FlattenJson(jsonObject, dataTable, string.Empty);
    
    return dataTable;
}

static void FlattenJson(JToken json, DataTable dataTable, string parentKey)
{
    foreach (var property in json.Children<JProperty>())
    {
        string currentKey = string.IsNullOrEmpty(parentKey) ? property.Name : $"{parentKey}.{property.Name}";

        if (property.Value.Type == JTokenType.Object)
        {
            FlattenJson(property.Value, dataTable, currentKey);
        }
        else if (property.Value.Type == JTokenType.Array)
        {
            for (int i = 0; i < property.Value.Count(); i++)
            {
                FlattenJson(property.Value[i], dataTable, $"{currentKey}[{i}]");
            }
        }
        else
        {
            dataTable.Rows.Add(currentKey, property.Value.ToString());
        }
    }
}

static void WriteDataTableToCsv(DataTable dataTable, string filePath)
{
    using (StreamWriter writer = new StreamWriter(filePath))
    {
        // Write header.
        writer.WriteLine(string.Join(",", dataTable.Columns));

        // Write rows.
        foreach (DataRow row in dataTable.Rows)
        {
            writer.WriteLine(string.Join(",", row.ItemArray));
        }
    }
}

// Load the JSON file.
string jsonData = File.ReadAllText("<YOUR_JSON_FILE>.json");
var jsonObject = JsonConvert.DeserializeObject<JObject>(jsonData);

// Create a DataTable to hold the multi-index structure.
DataTable dataTable = CreateMultiIndexDataTable(jsonObject);

// Save to CSV.
WriteDataTableToCsv(dataTable, "results.csv");

import json
import pandas as pd

# Load the JSON file.
data = json.loads(open("<YOUR_JSON_FILE>.json", "r", encoding="utf-8").read())

# Recursive function to flatten the JSON and create tuples for multi-index.
def flatten_json(obj, parent_key='', sep='_'):
    items = []
    for k, v in obj.items():
        new_key = f"{parent_key}{sep}{k}" if parent_key else k
        if isinstance(v, dict):
            items.extend(flatten_json(v, new_key, sep=sep).items())
        else:
            items.append((new_key, v))
    return dict(items)

# Flatten the JSON data.
flat_data = flatten_json(data)

# Split keys into a list for multi-index.
multi_index_tuples = [tuple(k.split('_')) for k in flat_data.keys()]
values = list(flat_data.values())

# Create the multi-index DataFrame.
multi_index = pd.MultiIndex.from_tuples(multi_index_tuples)
df = pd.DataFrame(values, index=multi_index, columns=['Value'])

# Save to CSV.
df.to_csv('results.csv')

View All Backtests

Follow these steps to view all of the backtests of a project:

1. Open the project that contains the backtests you want to view.
2. In the top-right corner of the IDE, click the Backtest Results icon.
   

A table containing all of the backtest results for the project is displayed. If there is a play icon to the left of the name, it's a backtest result . If there is a fast-forward icon next to the name, it's an optimization result .


3. (Optional) In the top-right corner, select the Show field and then select one of the options from the drop-down menu to filter the table by backtest or optimization results.
4. (Optional) In the bottom-right corner, click the Hide Error check box to remove backtest and optimization results from the table that had a runtime error.
5. (Optional) Use the pagination tools at the bottom to change the page.
6. (Optional) Click a column name to sort the table by that column.
7. Click a row in the table to open the results page of that backtest or optimization.

Rename Backtests

We give an arbitrary name (for example, "Smooth Apricot Chicken") to your backtest result files, but you can follow these steps to rename them:

1. Hover over the backtest you want to rename and then click the pencil icon that appears.

2. Enter the new backtest name and then click OK .

To programmatically set the backtest name, call the SetName set_name method.

SetName("Backtest Name");

self.set_name("Backtest Name")

For more information, see Set Name and Tags .

Clone Backtests

Hover over the backtest you want to clone, and then click the clone icon that appears to clone the backtest.

A new project is created with the backtest code files.

Delete Backtests

Hover over the backtest you want to delete, and then click the trash can icon that appears to delete the backtest.

Out of Sample Period

To reduce the chance of overfitting, organization managers can enforce all backtests must end a certain number of months before the current date. For example, if you set a one year out-of-sample period, the researchers on your team will not be able to use the most recent year of data in their backtests. A out-of-sample period is helpful because it leaves you a period to test your model after your done the development stage. Follow these steps to change the backtest out-of-sample period:

1. Open the organization homepage .
2. Scroll down to the Backtesting Out of Sample Period section.
3. Adjust the out-of-sample period duration or click on "No Holdout Period".

Errors

If a backtest produces more than 700 MB of data, then LEAN can't upload the results and the backtest results page appears empty.

Backtesting > Results > Portfolio Margin Plots

Introduction

The portfolio margin chart is a stacked area chart where each color represents the proportion of your available margin that a security consumes over time.

Margin plots can appear counter intuitive and hard to understand. To simplify things, this chart only draws the top 5-10 holdings at a given point in time. It group the smaller holdings into an area called "Others".

Sample Frequency

In backtests, the chart areas are sampled once a day to produce the plot, so intraday margin usage isn't visible.

Cash vs Margin

When you trade with a margin account, orders consume your cash before borrowing anything on margin. This order of precedence minimizes the interest charges on your margin loans.

This algorithm produces the following portfolio margin plot. When the initial trades fill, the orders consume available cash before borrowing anything available from the margin. Consuming available cash first avoids interest charges on the margin-loan and avoids margin calls during market downturns. This results in a relatively steady line hovering on 50% as there is no free cash to purchase with, but only the remaining 50% margin.

This algorithm is the same buy-and-hold strategy as the previous algorithm, but it only targets 50% exposure during the initial trades. The following portfolio margin plot shows the results. This version only consumes 16% of the available margin for each security, leaving a substantial amount of cash free. As the portfolio value grows, the fraction of the margin that this cash represents shrinks, showing an upward trending plot as the holdings of AAPL grow in value.

Examples

The following examples provide insight into portfolio margin calculations and show the functionality of the portfolio margin chart.

Buying Put Options

This algorithm demonstrates that when you buy a put Option contract and you long the underlying asset, it reduces the margin used by the underlying asset. In the following image, the gray area represents the underlying Equity and the blue area represents the put Option contract. The algorithm buys SPY on 10/07/2013, buys the put Option contract on 01/02/2014, and then exercises the contract on 06/21/2014.

Buying Options Spreads

This algorithm demonstrates that when you long and short two Option contracts at the same time, they combine together to reduce your margin usage. In the following image, the blue area represents a short position in a call Option contract and the gray area represents a long position in a call Option contract. The algorithm forms a bear call spread one leg at a time, shorting an in-the-money contract on 02/03/2023 and then longing an out-of-the-money contract on 02/14/2023.

Buying Futures

This algorithm demonstrates that Futures have a fixed, USD-denominated margin requirement. In the following image, the blue area represents the percentage of the available margin that the Futures contract uses. The algorithm buys the contract on 10/02/2023 and holds it until the end of the backtest. As the total portfolio value decreases during the last two week in the backtest, the amount of available margin in the portfolio decreases. However, the USD amount of margin used by the Futures contract is constant, so the proportion of portfolio margin used by the contract increases at the same time.

Buying Forex

This algorithm demonstrates how the 50x leverage available for Forex impacts the portfolio margin chart. In the following image, the blue area represents the percentage of the available margin that the Forex position uses. The algorithm buys 500% exposure to the EURUSD pair on 01/02/2018 and holds it until the end of the backtest. The portfolio margin chart shows that only 10% of the available margin is used to enter the trade since 500% / 50 = 10%.

Series Color Changes

The portfolio margin plot updates daily as the backtest runs. However, the color of a specific security in the plot isn't finalized until the end of the backtest. For instance, the following image shows an example plot during execution:

The following image shows what the plot looks like at the end of the backtest:

Series Names

The series names in the chart are the last known tickers for the respective assets (for example, SPY and XLE). Therefore, if you trade multiple assets throughout a backtest that share the same last known ticker (for example, FB in 1998 and FB in 2014), the chart labels them under the same series name.

Backtesting > Debugging

Introduction

The debugger is a built-in tool to help you debug coding errors while backtesting. The debugger enables you to slow down the code execution, step through the program line-by-line, and inspect the variables to understand the internal state of the program.

Breakpoints

Breakpoints are lines in your algorithm where execution pauses. You need at least one breakpoint in your code files to start the debugger. Open a project to start adjusting its breakpoints.

Add Breakpoints

Click to the left of a line number to add a breakpoint on that line.

Edit Breakpoint Conditions

Follow these steps to customize what happens when a breakpoint is hit:

1. Right-click the breakpoint and then click Edit Breakpoint... .
2. Click one of the options in the following table:

Enable and Disable Breakpoints

To enable a breakpoint, right-click it and then click Enable Breakpoint .

To disable a breakpoint, right-click it and then click Disable Breakpoint .

Follow these steps to enable and disable all breakpoints:

1. In the right navigation menu, click the Run and Debug icon.
2. In the Run and Debug panel, hover over the Breakpoints section and then click the Toggle Active Breakpoints icon.

Remove Breakpoints

To remove a breakpoint, right-click it and then click Remove Breakpoint .

Follow these steps to remove all breakpoints:

1. In the right navigation menu, click the Run and Debug icon.
2. In the Run and Debug panel, hover over the Breakpoints section and then click the Remove All Breakpoints icon.

Launch Debugger

Follow these steps to launch the debugger:

1. Open the project you want to debug.
2. In your project's code files, add at least one breakpoint.
3. Click the Debug icon.

If the Run and Debug panel is not open, it opens when the first breakpoint is hit.

Control Debugger

After you launch the debugger, you can use the following buttons to control it:

Button	Name	Default Keyboard Shortcut	Description
	Continue		Continue execution until the next breakpoint
	Step Over	Alt+F10	Step to the next line of code in the current or parent scope
	Step Into	Alt+F11	Step into the definition of the function call on the current line
	Restart	Shift+F11	Restart the debugger
	Disconnect	Shift+F5	Exit the debugger

Inspect Variables

After you launch the debugger, you can inspect the state of your algorithm as it executes each line of code. You can inspect local variables or custom expressions. The values of variables in your algorithm are formatted in the IDE to improve readability. For example, if you inspect a variable that references a DataFrame, the debugger represents the variable value as the following:

Local Variables

The Variables section of the Run and Debug panel shows the local variables at the current breakpoint. If a variable in the panel is an object, click it to see its members. The panel updates as the algorithm runs.

Follow these steps to update the value of a variable:

1. In the Run and Debug panel, right-click a variable and then click Set Value .
2. Enter the new value and then press Enter .

Custom Expressions

The Watch section of the Run and Debug panel shows any custom expressions you add. For example, you can add an expression to show the current date in the backtest.

Follow these steps to add a custom expression:

1. Hover over the Watch section and then click the plus icon that appears.
2. Enter an expression and then press Enter .

Backtesting > Report

Introduction

Reports provide a summary of your algorithm's performance. They outline key statistics, returns, and performance during various market crises. You can generate a performance report after your backtest completes and download the report as a PDF.

Key Statistics

The top of the backtest report displays statistics to summarize your algorithm's performance. The following table describes the key statistics in the report:

Returns

The backtest report displays charts to show the algorithm's returns per trade, per day, per month, per year, and the cumulative returns over the backtest.

Returns per Trade

This chart displays a histogram that shows the distribution of returns per trade over the backtesting period.

Daily Returns

This chart displays the returns of each day. Blue bars represent profitable days and gray bars represent unprofitable days.

Monthly Returns

This chart displays the return of each month. We convert the original equity curve series into a monthly series and calculate the returns of each month. Green cells represent months with a positive return and red cells represent months with a negative return. Months that have a greater magnitude of returns are represented with darker cells. Yellow cells represent months with a relatively small gain or loss. White rectangles represent months that are not included in the backtest period. The values in the cells are percentages.

Annual Returns

This chart displays the return of each year. We calculate the total return within each year and represent each year with a blue bar. The red dotted line represents the average of the annual returns.

Cumulative Returns

This chart displays the cumulative returns of your algorithm. The blue line represents your algorithm and the gray line represents the benchmark.

Asset Allocation

This chart displays a time-weighted average of the absolute holdings value for each asset that entered your portfolio during the backtest. When an asset has a percentage that is too small to be shown in the pie chart, it is incorporated into an "Others" category.

Drawdown

This chart displays the peak-to-trough drawdown of your portfolio's equity throughout the backtest period. The drawdown of each day is defined as the percentage loss since the maximum equity value before the current day. The drawdowns are calculated based on daily data. The top 5 drawdown periods are marked in the chart with different colors.

Rolling Statistics

The backtest report displays time series for your portfolio's rolling beta and Sharpe ratio .

Rolling Portfolio Beta

This chart displays the rolling portfolio beta over trailing 6 and 12 month periods. The light blue line represents the 6 month period and the dark blue line represents the 12 month period.

Rolling Sharpe Ratio

This chart displays the rolling portfolio Sharpe ratio over trailing 6 and 12 month periods. The light blue line represents the 6 month period and the dark blue line represents the 12 month period.

Exposure

The backtest report displays time series for your portfolio's overall leverage and your portfolio's long-short exposure by asset class.

Leverage

This chart displays your algorithm's utilization of leverage over time.

Long-Short Exposure By Asset Class

This chart displays your algorithm's long-short exposure by asset class over time.

Crisis Events

This set of charts displays the cumulative returns of your algorithm and the benchmark during various historical periods. The blue line represents the cumulative returns of your algorithm and the grey line represents the cumulative return of the benchmark. The report only contains the crisis event that occurred during your algorithm's backtest period. The following table shows the crisis events that may be included in your backtest report:

Parameters

This section of the report shows the name and value of all the parameters in your project.

Customize Reports

To create custom reports, customize the HTML and CSS.

Customize the Report HTML

The Report / template.html file in the LEAN GitHub repository defines the stucture of the reports you generate. To override the HTML file, add a report.html file to your project . To include some of the information and charts that are in the default report, use the report keys in the Report / ReportKey.cs file in the LEAN GitHub repository. For example, to add the Sharpe ratio of your backtest to the custom HTML file, use {{$KPI-SHARPE}} .

To include the crisis event plots in your report, add the {{$HTML-CRISIS-PLOTS}} key and then define the structure of the individual plots inside of <!--crisis and crisis--> . Inside of this comment, you can utilize the {{$TEXT-CRISIS-TITLE}} and {{$PLOT-CRISIS-CONTENT}} keys. For example, the following HTML is the default format for each crisis plot:

<!--crisis
<div class="col-xs-4">
    <table class="crisis-chart table compact">
        <thead>
        <tr>
            <th style="display: block; height: 75px;">{{$TEXT-CRISIS-TITLE}}</th>
        </tr>
        </thead>
        <tbody>
        <tr>
            <td style="padding:0;">
                <img src="{{$PLOT-CRISIS-CONTENT}}">
            </td>
        </tr>
        </tbody>
    </table>
</div>
crisis--&gt

To include the algorithm parameters in your report, add the {{$PARAMETERS}} key and then define the HTML element inside of <!--parameters and parameters--> . Inside of this comment, you can use special keys {{$KEY<parameterIndex>}} and {{$VALUE<parameterIndex>}} , which represent the key and value of a single parameter. For example, the following HTML is the default format for the parameters element:

<!--parameters
<tr>
    <td class = "title"> {{$KEY0}} </td><td> {{$VALUE0}} </td>
    <td class = "title"> {{$KEY1}} </td><td> {{$VALUE1}} </td>
</tr>
parameters--&gt

In the preceding example, {{$KEY0}} is the name of the first parameter in the algorithm and {{$VALUE0}} is its value.

Customize the Report CSS

The Report / css / report.css file in the LEAN GitHub repository defines the style of the reports you generate. To override the stylesheet, add a report.css file to your project .

Backtesting > Engine Performance

Introduction

A set of benchmark algorithms are periodically run to test the status and speed of the Lean master branch. View the Lean Performance Benchmark page to see the results. The chart at the top of the page shows the data points per second for each of the benchmark algorithms. The table at the bottom of the page shows the benchmark algorithms that are run to produce the results.

Datasets

Datasets > Navigating the Market

Introduction

Datasets are a stream of data points you use in your algorithms to make trading decisions and fill orders. Our Dataset Market is a portal where we curate datasets available for use in your algorithms. It includes price, fundamental, and alternative datasets. Consider using fundamental and alternative datasets to incorporate more information in your trading decisions. Fundamental and alternative datasets contain information that is not present in the price. Price data is commonly researched for trading ideas, so you may find it easier to discover alpha in other types of datasets.

The Dataset Market enables you to easily load datasets into your trading algorithms for use in the cloud or locally. The datasets come configured ready to integrate into your research and backtesting without any need for cleaning. The datasets in our market are vetted by the QuantConnect team to be high-quality, contain actionable information, and be free of survivorship-bias. Our Dataset Market is growing quickly, as new datasets are added frequently.

The Dataset Market is a place where you can view, subscribe to, and download datasets. We provide an example algorithm for each dataset that you can clone to easily get started using the new dataset. We also provide an example research notebook for most datasets to demonstrate how to use the dataset in the Research Environment. You can always view the dataset reviews to learn about the experience other members have had using the dataset. This page explains the layout of the Dataset Market to help you navigate the marketplace.

View All Listings

The Dataset Market displays all our supported datasets. To view the page, in the top navigation bar, click Data.

Each dataset displays the name, description, coverage, start date, and price of the dataset. Coverage is the number of assets, securities, contracts, currency pairs, or articles that are included in the dataset. To view the listing page of a dataset, click the dataset.

You can search the market by applying filters or searching for keywords.

Filter Listings by Category

Click the Category: All field and then select a category from the drop-down menu to only display datasets in that category.

Filter Listings by Delivery Options

Click the Delivery Options: All field and then select an option from the drop-down menu to only display datasets with that delivery option.

Search for Keywords

Enter keywords in the search bar to only display datasets that contain those keywords.

Homepage

The homepage of a dataset listing displays everything that you need to get started using the dataset. The following table describes the tabs on the homepage:

The following table describes the sections displayed under the About tab for most datasets:

Sidebar

The sidebar of the dataset listing provides a brief summary of the dataset. The following table describes the summary content:

Documentation

The Documentation tab on a dataset listing demonstrates how to use the dataset. The documentation covers requesting the data, accessing the data in your algorithm, and performing history requests. We provide documentation in C# and Python so you can easily integrate the dataset into your algorithms, regardless of the programming language you use.

The Documentation tab also has a Data Point Attributes section to show the dataset's attributes. If an attribute has a custom data type, you can click the attribute to view the attributes of the custom data type.

Factor Research

Some dataset listings have a Research tab that displays an analysis of the data point attributes in the dataset. Follow these steps to clone the example research notebook of a dataset:

1. Log in to the Algorithm Lab.
2. Open a dataset listing page .
3. Click the Research tab, if available.
4. Click Clone This Notebook .
5. Click Clone Algorithm .

Examples

The Examples tab on a dataset listing shows how to use the dataset in a trading algorithm. We provide examples in C# and Python for both of the classic and framework algorithm designs. Copy-paste these example algorithms to jumpstart your strategy research. Consider adjusting the strategy to make it your own or using the parameter optimization feature to improve the performance of the algorithms.

Licenses

The Licensing tab shows the available licenses for the dataset. Each dataset comes with its own licensing requirements, depending on the data vendor. For more information about licensing types, see Licensing .

Reviews

The bottom of the dataset listing page shows reviews published by QuantConnect members. You can sort, filter, and write dataset reviews.

Sort Reviews

Open a dataset listing page , click the Most Recent field, and then select a metric from the drop-down menu to sort the reviews by that metric.

Filter Reviews

Open a dataset listing page , click the All field, and then select a number of stars from the drop-down menu to only display the reviews with that rating.

Write Reviews

Follow these steps to write a review:

1. Log in to the Algorithm Lab.
2. Open a dataset listing page .
3. At the bottom of the page, select a number of stars to give your review.
4. Write your review.
5. Click Submit Review .

Datasets > Categories

Introduction

Dataset categories are a way to identify different types of datasets in our Dataset Market. We provide many price, fundamental, and alternative datasets for you to use in your research and trading. Datasets that include factors outside of the security price are less researched, so they may have more alpha to discover. Incorporate alternative datasets into your algorithms so that you can make more informed trading decisions.

Geospatial

Geospatial data is data related to objects that have a position in the world.

Commerce

Commerce data is data on customer and business behavior.

Financial Market

Financial market data is data on the trading activity on exchanges.

Consumer

Consumer data is data on all aspects of consumers, including online shopping behaviors, consumer demographics, and consumer attitudes.

B2B

Business-to-business (B2B) data is data on businesses that sell goods and services to other businesses.

Transport and Logistics

Transport and logistics data is data on the transportation of goods and the logistics of the transportation.

Environmental

Environmental data is data on the state of the environment, including meteorological data, biodiversity data, and pollution data.

Credit Rating

Credit rating data is data on the financial position of individuals and businesses.

Real Estate

Real estate data is data on residential and commercial real estate, including ownership data, real estate listing data, and real estate demographic data.

Web

Web data is data on the behavior of internet users.

Legal

Legal data is data on the law, including new regulations, government website data, and litigation history.

Health Care

Healthcare data is data on patient-doctor visits, including claims data, fitness wearables data, and health record data.

Entertainment

Entertainment data is data on the media consumption preferences and behaviors of consumers.

Energy

Energy data is data on energy production, distribution, and consumption.

Industry

Industry data is data on various groupings in the economy.

Political

Political data is data that's collected on political activity, including election votes and political party policies.

News and Events

News and events data is data that's collected from news providers regarding current events.

Datasets > Data Issues

Introduction

Data issues are incorrect or missing values in a dataset. These issues are generally a result of human error or from mistakes in the data collection process. Data issues can be reported by any QuantConnect member. When data issues are reported and verified, our Data Team works to quickly resolve them. Thanks to the communal efforts of the QuantConnect members, the QuantConnect data is reviewed and fixed by over 250,000 people, giving you a very high-quality source of data.

Common Issues

Data issues can occur in both historical and live data providers. Some common examples of data issues include the following:

• Missing or incorrect values
• Splits and dividends
• Listings and delistings
• Ticker changes

View Current Issues

To view the list of current data issues, log in to the Algorithm Lab and then, in the left navigation bar, click Datasets > Data Issues . Before you report a new issue, review the list of current issues to ensure that the issue is not already reported. The number of open data issues can sometimes be large, but our Data Team works on resolving them as quickly as possible while prioritizing the most important ones.

Report New Issues

When all of the QuantConnect members report the data issues that they find, we can ensure the datasets are high quality for everyone. The easier it is for our Data Team to detect and reproduce the issues you report, the faster we can resolve them. If you encounter an issue with live data, email us a description of the issue. If you find an issue with the historical data of a dataset, follow these steps to report it:

1. Log in to the Algorithm Lab.
2. Open the Data Explorer Issues page.
3. On the Data Explorer page, fill out the form.
4. Follow these steps to attach a backtest or notebook that demonstrates the issue:
1. Click Attach Backtest .
2. Click the Project field and then select the project from the drop-down menu.
3. Click the Backtest field and then select the backtest from the drop-down menu.
5. Click Publish .

Datasets > Misconceptions

Introduction

Some data issues are reported that aren't actually data issues. Instead, they are from a misunderstanding of how the data is collected, timestamped, formatted, and normalized. These misunderstandings are caused by assumptions that the data should be the same across different platforms, should have the same timezones, should be timestamped a certain way, and should be normalized the same as other data sources.

Cross-Platform Discrepancies

You may find our data can sometimes be slightly different from the data that's displayed on other platforms. Most of the differences occur because our data is institutional quality while a lot of the other platforms use a cheaper alternative. We use the Consolidated Tape Association (CTA) and Unlisted Trading Privileges (UTP) tick feeds, which cover the entire US tick feed. In contrast, most charting websites use the Better Alternative Trading System (BATS), which has very permissive display policies but only covers about 6-7% of the total market volume . Our tick feed doesn't include over-the-counter (OTC) trades, but the data on other platforms like Yahoo Finance include OTC trades.

Timezone Differences

Datasets all have different timezones. Most price datasets are timestamped in Eastern Time (ET). However, Future markets have more exotic timezones, depending on where the Future contract is trading. QuantConnect allows the raw data to be in different timezones. For US Equities, the timezone is ET. For Forex prices, the timezone is Coordinated Universal Time (UTC). In contrast, other charting platforms may display data with ET timestamps. Forex uses UTC, but CFD uses timezones relative to each of the CFD products that lists. QuantConnect accurately reflects all of these timezones from the relative markets that they're trading.

Misaligned Timestamps

Every piece of data has a period. Some data is near-instantaneous, like tick data. Other data has a longer period, like second, minute, hour, and daily bars. QuantConnect delivers this data to your algorithms at the end of the period to ensure that lookahead bias doesn’t occur. When you look at the Time time property of your algorithm, the period has already ended, so it looks as if the data is offset by one period. To compare the timestamps of our data to other data, use the Time time property of the current bar. The Time time property of the bar is the start of the bar and the EndTime end_time property is the end of the bar. If you use Python and request historical data, the time index in the DataFrame that's returned maps to the EndTime end_time of the respective bar. For more information about timestamps, see Time Modeling .

Data Normalization

The data normalization mode defines how historical data is adjusted to accommodate for splits, dividends, and continuous Future contract roll overs. When you compare the data in the Dataset Market to data that's hosted on other platforms, the data may have different values because a different data normalization mode is being used to adjust the data. Ensure datasets are using the same normalization mode before reporting data issues. The most common way to recognize this bug is by comparing the two price series and seeing them significantly deviate in the past. The following data normalization modes are available:

Adjusted Prices

By default, LEAN adjusts US Equity data for splits and dividends to produce a smooth price curve. We use the entire split and dividend history to adjust historical prices. This process ensures you get the same adjusted prices, regardless of the backtest end date.

Backtest differences occur when you run backtests before a split or dividend occurs in live trading and then run the same backtest after it occurs. The second time you run the backtest, the adjusted prices will be different so it can cause different backtest results. The difference can be significant in large universes because of multiple corporate actions and the cummulative effect of orders with a small difference.

Opening and Closing Auctions

The opening and closing price of the day is set by very specific opening and closing auction ticks. When a stock like Apple is listed, it’s listed on Nasdaq. The open auction tick on Nasdaq is the price that’s used as the official open of the day. NYSE, BATS, and other exchanges also have opening auctions, but the only official opening price for Apple is the opening auction on the exchange where it was listed.

We set the opening and closing prices of the first and last bars of the day to the official auction prices. This process is used for second, minute, hour, and daily bars for the 9:30 AM and 4:00 PM Eastern Time (ET) prices. In contrast, other platforms might not be using the correct opening and closing prices.

The official auction prices are usually emitted 2-30 seconds after the market open and close. We do our best to use the official opening and closing prices in the bars we build, but the delay can be so large that there isn't enough time to update the opening and closing price of the bar before it's injected into your algorithms. For example, if you subscribe to second resolution data, we wait until the end of the second for the opening price but most second resolution data won’t get the official opening price. If you subscribe to minute resolution data, we wait until the end of the minute for the opening auction price. Most of the time, you’ll get the actual opening auction price with minute resolution data, but there are always exceptions. Nasdaq and NYSE can have delays in publishing the opening auction price, but we don’t have control over those issues and we have to emit the data on time so that you get the bar you are expecting.

Live and Backtesting Differences

In live trading, bars are built using the exchange timestamps with microsecond accuracy. This microsecond-by-microsecond processing of the ticks can mean that the individual bars between live trading and backtesting can have slightly different ticks. As a result, it's possible for a tick to be counted in different bars between backtesting and live trading, which can lead to bars having slightly different open, high, low, close, and volume values.

There is a delay in when new live data is available for backtesting. It's normally available after 24-48 hours. If you need to closely monitor new data, use live paper trading .

Datasets > Licensing

Introduction

You can license datasets in the Dataset Market to use in the cloud for live trading and research or to download locally. We have contracts with the data providers in the Dataset Market that define the costs of each license. All of the datasets can be used in QuantConnect Cloud. There are some free licenses, but we can't freely redistribute most of the datasets.

Free

We strive to make as many datasets available for free to use in the cloud and to download locally as possible. We also list proprietary datasets that are available for license using our cloud or download paid licensing. Most price data is free for use in the cloud.

Cloud

If you have a Cloud license for a dataset, you can access the dataset for research, backtests, and live trading in the Algorithm Lab. The cost of the license is added to your monthly bill, which you can pay with your organization's credit card or QuantConnect Credit balance. With one Cloud license for a dataset, all of the members in your organization can access the dataset in the cloud.

Add Cloud Access

You need an organization above the Free tier to purchase cloud access to datasets.

Follow these steps to add cloud access to datasets:

1. Log in to the Algorithm Lab.
2. Open the listing page of a dataset for which you want to gain cloud access.
3. On the dataset listing page, click the Pricing tab.
4. Under the Cloud Access section, click SUBSCRIBE .
5. On the Pricing page, click Proceed to Checkout .

Remove Cloud Access

Follow these steps to remove cloud access to datasets:

1. Open the organization homepage .
2. Click Edit Plan .
3. On the pricing page, click the Customize Plan > Build Your Own Pack > Data tab.
4. In the Datasets Subscriptions section, next to the name of the dataset you want to remove, click Added .
5. Click Proceed to Checkout .

Download

If you have a Download license, you can store datasets on your local machine. This download is for the licensed organization's internal LEAN use only and cannot be redistributed or converted in any format. If you study the data and produce some charts, you may share images of the charts online if the original data can't be to reconstructed from the image. The cost of the license depends on the dataset and it's calculated on a per-file or per-day basis. For more information about downloading datasets, see Downloading Data . If you bulk download datasets, you can download historical data packages or daily updates. In most cases, you need both.

Datasets > Vendors

Introduction

We welcome submissions of new datasets by data companies. Review our submission process to learn how to get your dataset listed on QuantConnect.

Submission Criteria

Datasets must meet the following criteria to be considered for the Datasets Market:

• A well-defined dataset with a clear and static vision for the data to minimize churn or changes.
• Robust ticker and security links to ensure the tickers are tracked well through time. ISIN, FIGI, or point-in-time tickers are supported.
• Sufficient organizational funding to ensure at least 1 year of operation.
• Reliable API with no dead links or 502 errors.
• Consistent delivery schedule with data delivered on time and in time for trading.
• Consistent data format with notifications and lead time on data format updates.
• At least 1 year of historical data.
• Free of survivorship bias.
• Good documentation of the dataset.

If the dataset is alternative data, in addition to the criteria above, the dataset must practice the Alternative Investment Standards defined by the non-profit Investment Data Standards Organization (IDSO). The Alternative Investment Standards outline the rules and best practices for collecting and distributing alternative datasets. For example, the AWS Web Crawling Best Practices publication states that “when a web crawler encounters a robots.txt file on a website, it parses the instructions and adjusts its crawling behavior accordingly”.

Review Process

The dataset review process is in place to ensure that your dataset matches the submission criteria. The review process can take several weeks. If your dataset is accepted, we'll begin integrating it into the Datasets Market. If your dataset is rejected, we'll provide feedback to help you get the dataset accepted.

Contributing Datasets

We encourage you to integrate your own datasets. To integrate your dataset, see the Contributing Datasets tutorial. The integration process only takes about 1 day of engineering.

Give Free Trials

Follow these steps to give a free trial of your dataset to a QuantConnect organization:

1. Log in to the Algorithm Lab.
2. Open the Dataset Market .
3. On the Datasets page, click the dataset that you want to give as a trial.
4. In the right sidebar of the dataset listing, click Dashboard .
5. On the dataset dashboard page, click give trial .
6. In the Give Trial to Organization window, enter the expriation date of the trial and then click OK .
7. In the Organization Owners Email window, enter the email address of the member who owns the organization that you want to grant the trial.
8. If the email address you entered owns mulitple organizations, in the Select target organization window, select an organization from the drop-down menu and then click OK .
9. Click OK .

Contacting Our Team

If you want to discuss integrating your dataset into the Datasets Market, contact us . We look forward to working with you so that we can provide QuantConnect members with access to more high-quality datasets.

Datasets > QuantConnect

Datasets > QuantConnect > US Equities

Introduction

The QuantConnect data provider provides a stream of US Equity trades and quotes to your trading algorithm during live execution. Live data enables you to make real-time trades and update the value of the securities in your portfolio.

The QuantConnect data provider also provides a live stream of corporate actions (US Equity Security Master), daily updates on company fundamentals (US Fundamentals), and the number of shares that are available for short sellers to borrow (US Equities Short Availability).

Sourcing

The QuantConnect data provider consolidates US Equity market data across all of the exchanges. Over-the-Counter (OTC) trades are excluded. The data is powered by the Securities Information Processor (SIP), so it has 100% market coverage. In contrast, free platforms that display data feeds like the Better Alternative Trading System (BATS) only have about 6-7% market coverage .

We provide live splits, dividends, and corporate actions for US companies. We deliver them to your algorithm before the trading day starts.

Universe Selection

The QuantConnect data provider enables you to create a dynamic universe of US Equities.

Fundamental Universe

The live data for fundamental universe selection arrives at 6/7 AM Eastern Time (ET), so fundamental universe selection runs for live algorithms between 7 and 8 AM ET. This timing allows you to place trades before the market opens. Don't schedule anything for midnight because the universe selection data isn't ready yet.

// Run universe selection asynchronously to speed up your algorithm.
UniverseSettings.Asynchronous = true;
AddUniverse(SelectFundamental);

#  Run universe selection asynchronously to speed up your algorithm.
self.universe_settings.asynchronous = True
self.add_universe(self._select_fundamental)

ETF Constituent Universe

The QuantConnect data provider enables you to create a universe of securities to match the constituents of an ETF. For more information about ETF universes, see ETF Constituents Selection .

// Request an async universe of stocks that match the ETF constituents of SPY
UniverseSettings.Asynchronous = true;
var spy = AddEquity("SPY").Symbol;
AddUniverse(Universe.ETF(spy, UniverseSettings, ETFConstituentsFilter));

# Request an async universe of stocks that match the ETF constituents of SPY
self.universe_settings.asynchronous = True
spy = self.add_equity("SPY").symbol
self.add_universe(self.universe.etf(spy, self.universe_settings, self._etf_constituents_filter))

Bar Building

We aggregate ticks to build bars.

Discrepancies

In live trading, bars are built using the exchange timestamps with microsecond accuracy. This microsecond-by-microsecond processing of the ticks can mean that the individual bars between live trading and backtesting can have slightly different ticks. As a result, it's possible for a tick to be counted in different bars between backtesting and live trading, which can lead to bars having slightly different open, high, low, close, and volume values.

Opening and Closing Auctions

The opening and closing price of the day is set by very specific opening and closing auction ticks. When a stock like Apple is listed, it’s listed on Nasdaq. The open auction tick on Nasdaq is the price that’s used as the official open of the day. NYSE, BATS, and other exchanges also have opening auctions, but the only official opening price for Apple is the opening auction on the exchange where it was listed.

We set the opening and closing prices of the first and last bars of the day to the official auction prices. This process is used for second, minute, hour, and daily bars for the 9:30 AM and 4:00 PM Eastern Time (ET) prices. In contrast, other platforms might not be using the correct opening and closing prices.

The official auction prices are usually emitted 2-30 seconds after the market open and close. We do our best to use the official opening and closing prices in the bars we build, but the delay can be so large that there isn't enough time to update the opening and closing price of the bar before it's injected into your algorithms. For example, if you subscribe to second resolution data, we wait until the end of the second for the opening price but most second resolution data won’t get the official opening price. If you subscribe to minute resolution data, we wait until the end of the minute for the opening auction price. Most of the time, you’ll get the actual opening auction price with minute resolution data, but there are always exceptions. Nasdaq and NYSE can have delays in publishing the opening auction price, but we don’t have control over those issues and we have to emit the data on time so that you get the bar you are expecting.

Excluded Ticks

The bar-building process can exclude ticks. If a tick is excluded, its volume is aggregated in the bar but its price is not aggregated in the bar. Ticks are excluded if any of the following statements are true:

• The tick is suspicious.
• The tick is from the FINRA exchange and meets our price and volume thresholds.
• The trade has none of the following included TradeConditionFlags and at least one of the following excluded TradeConditionFlags :

TradeConditionFlags	Status	Description
Regular REGULAR	Included	A trade made without stated conditions is deemed the regular way for settlement on the third business day following the transaction date.
FormT FORM_T	Included	Trading in extended hours enables investors to react quickly to events that typically occur outside regular market hours, such as earnings reports. However, liquidity may be constrained during such Form T trading, resulting in wide bid-ask spreads.
Cash CASH	Included	A transaction that requires delivery of securities and payment on the same day the trade takes place.
ExtendedHours EXTENDED_HOURS	Included	Identifies a trade that was executed outside of regular primary market hours and is reported as an extended hours trade.
NextDay NEXT_DAY	Included	A transaction that requires the delivery of securities on the first business day following the trade date.
OfficialClose OFFICIAL_CLOSE	Included	Indicates the "official" closing value determined by a Market Center. This transaction report will contain the market center generated closing price.
OfficialOpen OFFICIAL_OPEN	Included	Indicates the 'Official' open value as determined by a Market Center. This transaction report will contain the market center generated opening price.
ClosingPrints CLOSING_PRINTS	Included	The transaction that constituted the trade-through was a single priced closing transaction by the Market Center.
OpeningPrints OPENING_PRINTS	Included	The trade that constituted the trade-through was a single priced opening transaction by the Market Center.
IntermarketSweep INTERMARKET_SWEEP	Excluded	The transaction that constituted the trade-through was the execution of an order identified as an Intermarket Sweep Order.
TradeThroughExempt TRADE_THROUGH_EXEMPT	Excluded	Denotes whether or not a trade is exempt (Rule 611).
OddLot ODD_LOT	Excluded	Denotes the trade is an odd lot less than a 100 shares.

• The quote has a size of less than 100 shares.
• The quote has none of the following included QuoteConditionFlags and at least one of the following excluded QuoteConditionFlags :
• The quote has one of the following QuoteConditionFlags :

QuoteConditionFlags	Status	Description
Closing CLOSING	Included	Indicates that this quote was the last quote for a security for that Participant.
NewsDissemination NEWS_DISSEMINATION	Included	Denotes a regulatory trading halt when relevant news influencing the security is being disseminated. Trading is suspended until the primary market determines that an adequate publication or disclosure of information has occurred.
NewsPending NEWS_PENDING	Included	Denotes a regulatory Trading Halt due to an expected news announcement, which may influence the security. An Opening Delay or Trading Halt may be continued once the news has been disseminated.
TradingRangeIndication TRADING_RANGE_INDICATION	Included	Denotes the probable trading range (Bid and Offer prices, no sizes) of a security that is not Opening Delayed or Trading Halted. The Trading Range Indication is used prior to or after the opening of a security.
OrderImbalance ORDER_IMBALANCE	Included	Denotes a non-regulatory halt condition where there is a significant imbalance of buy or sell orders.
Resume RESUME	Included	Indicates that trading for a Participant is no longer suspended in a security that had been Opening Delayed or Trading Halted.
Regular REGULAR	Excluded	This condition is used for the majority of quotes to indicate a normal trading environment.
Slow SLOW	Excluded	This condition is used to indicate that the quote is a Slow Quote on both the bid and offer sides due to a Set Slow List that includes high price securities.
Gap GAP	Excluded	While in this mode, auto-execution is not eligible, the quote is then considered manual and non-firm in the bid and offer, and either or both sides can be traded through as per Regulation NMS.
OpeningQuote OPENING_QUOTE	Excluded	This condition can be disseminated to indicate that this quote was the opening quote for a security for that Participant.
FastTrading FAST_TRADING	Excluded	For extremely active periods of short duration. While in this mode, the UTP Participant will enter quotations on a best efforts basis.
Resume RESUME	Excluded	Indicate that trading for a Participant is no longer suspended in a security which had been Opening Delayed or Trading Halted.

In the preceding tables, Participant refers to the entities on page 19 of the Consolidated Tape System Multicast Output Binary Specification .

Suspicious Ticks

Tick price data is raw and unfiltered, so it can contain a lot of noise. If a tick is not tradable, we flag it as suspicious. This process makes the bars a more realistic representation of what you could execute in live trading. If you use tick data, avoid using suspicious ticks in your algorithms as informative data points. We recommend only using tick data if you understand the risks and are able to perform your own tick filtering. Ticks are flagged as suspicious in the following situations:

• The tick occurs below the best bid or above the best ask


This image shows a tick that occurred above the best ask price of a security. The green line represents the best ask of the security, the blue line represents the best bid of the security, and the red dots represent trade ticks. The ticks between the best bid and ask occur from filling hidden orders. The tick that occurred above the best ask price is flagged as suspicious.

• The tick occurs far from the current market price


This image shows a tick that occurred far from the price of the security. The red dots represent trade ticks. The tick that occurred far from the market price is flagged as suspicious.

• The tick occurs on a dark pool
• The tick is rolled back
• The tick is reported late

Delivery

Most live trading algorithms run on co-located servers racked in Equinix. Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters.

Live data takes time to travel from the source to your algorithm. The QuantConnect latencies vary depending on the data provider, but for US Equities, we have a latency of 5-40 milliseconds. A much more significant source of latency is the round trip order times from brokers, which can vary from 100ms to 5 seconds. QuantConnect is not intended for high-frequency trading, but we have integrations to high-speed brokers if you need.

Historical Data

When you request historical data or run warm-up , the amount of historical data you can access depends on the resolution of your data subscriptions. The following table shows the amount of trailing historical data you can access for each data resolution:

Pricing

The QuantConnect data provider serves US Equities data for free.

Datasets > QuantConnect > Crypto

Introduction

The QuantConnect data provider provides a stream of Crypto trades and quotes to your trading algorithm during live execution. Live data enables you to make real-time trades and update the value of the securities in your portfolio.

Sourcing

The QuantConnect data provider uses WebSockets to gather Crypto market data from the following sources:

• Binance & Binance US
• Bitfinex
• Bybit
• Coinbase
• Kraken

Universe Selection

The QuantConnect data provider enables you to create a dynamic universe of Crypto securities. The live data for Crypto universe selection arrives at 4 PM Coordinated Universal Time (UTC), so universe selection runs for live algorithms between 4 PM and 4:30 PM. Don't schedule anything for midnight because the universe selection data isn't ready yet.

// Request an async Bitfinex crypto universe universe with a selection filter.
UniverseSettings.Asynchronous = true;
AddUniverse(CryptoUniverse.Bitfinex(UniverseSelectionFilter));

# Request an async Bitfinex crypto universe universe with a selection filter.
self.universe_settings.asynchronous = True
self.add_universe(CryptoUniverse.bitfinex(self._universe_selection_filter))

To view an example for each Crypto market, see the Universe Selection section of the Crypto market in the Dataset Market .

Bar Building

We aggregate ticks to build bars.

In live trading, bars are built using the exchange timestamps with microsecond accuracy. This microsecond-by-microsecond processing of the ticks can mean that the individual bars between live trading and backtesting can have slightly different ticks. As a result, it's possible for a tick to be counted in different bars between backtesting and live trading, which can lead to bars having slightly different open, high, low, close, and volume values.

Delivery

Most live trading algorithms run on co-located servers racked in Equinix. Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters.

Live data takes time to travel from the source to your algorithm. The QuantConnect latencies vary depending on the data provider, but for US Equities, we have a latency of 5-40 milliseconds. A much more significant source of latency is the round trip order times from brokers, which can vary from 100ms to 5 seconds. QuantConnect is not intended for high-frequency trading, but we have integrations to high-speed brokers if you need.

Historical Data

When you request historical data or run warm-up , the amount of historical data you can access depends on the resolution of your data subscriptions. The following table shows the amount of trailing historical data you can access for each data resolution:

Pricing

The QuantConnect data provider serves Crypto data for free.

Datasets > QuantConnect > Crypto Futures

Introduction

The QuantConnect data provider provides a stream of Crypto Futures trades and quotes to your trading algorithm during live execution. Live data enables you to make real-time trades and update the value of the securities in your portfolio.

Sourcing

The QuantConnect data provider uses WebSockets to gather Crypto Futures market data from Binance.

Bar Building

We aggregate ticks to build bars.

In live trading, bars are built using the exchange timestamps with microsecond accuracy. This microsecond-by-microsecond processing of the ticks can mean that the individual bars between live trading and backtesting can have slightly different ticks. As a result, it's possible for a tick to be counted in different bars between backtesting and live trading, which can lead to bars having slightly different open, high, low, close, and volume values.

Delivery

Most live trading algorithms run on co-located servers racked in Equinix. Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters.

Live data takes time to travel from the source to your algorithm. The QuantConnect latencies vary depending on the data provider, but for US Equities, we have a latency of 5-40 milliseconds. A much more significant source of latency is the round trip order times from brokers, which can vary from 100ms to 5 seconds. QuantConnect is not intended for high-frequency trading, but we have integrations to high-speed brokers if you need.

Historical Data

When you request historical data or run warm-up , the amount of historical data you can access depends on the resolution of your data subscriptions. The following table shows the amount of trailing historical data you can access for each data resolution:

Pricing

The QuantConnect data provider serves Crypto Futures data for free.

Datasets > QuantConnect > CFD

Introduction

The QuantConnect data provider provides a stream of CFD trades and quotes to your trading algorithm during live execution. Live data enables you to make real-time trades and update the value of the securities in your portfolio.

Sourcing

The QuantConnect data provider consolidates CFD market data from OANDA.

Bar Building

We aggregate ticks to build bars.

In live trading, bars are built using the exchange timestamps with microsecond accuracy. This microsecond-by-microsecond processing of the ticks can mean that the individual bars between live trading and backtesting can have slightly different ticks. As a result, it's possible for a tick to be counted in different bars between backtesting and live trading, which can lead to bars having slightly different open, high, low, close, and volume values.

Delivery

Most live trading algorithms run on co-located servers racked in Equinix. Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters.

Live data takes time to travel from the source to your algorithm. The QuantConnect latencies vary depending on the data provider, but for US Equities, we have a latency of 5-40 milliseconds. A much more significant source of latency is the round trip order times from brokers, which can vary from 100ms to 5 seconds. QuantConnect is not intended for high-frequency trading, but we have integrations to high-speed brokers if you need.

Historical Data

When you request historical data or run warm-up , the amount of historical data you can access depends on the resolution of your data subscriptions. The following table shows the amount of trailing historical data you can access for each data resolution:

Pricing

The QuantConnect data provider serves CFD data for free.

Datasets > QuantConnect > Forex

Introduction

The QuantConnect data provider provides a stream of Forex pair trades and quotes to your trading algorithm during live execution. Live data enables you to make real-time trades and update the value of the securities in your portfolio.

Sourcing

The QuantConnect data provider consolidates Forex market data from OANDA.

Bar Building

We aggregate ticks to build bars.

In live trading, bars are built using the exchange timestamps with microsecond accuracy. This microsecond-by-microsecond processing of the ticks can mean that the individual bars between live trading and backtesting can have slightly different ticks. As a result, it's possible for a tick to be counted in different bars between backtesting and live trading, which can lead to bars having slightly different open, high, low, close, and volume values.

Delivery

Most live trading algorithms run on co-located servers racked in Equinix. Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters.

Live data takes time to travel from the source to your algorithm. The QuantConnect latencies vary depending on the data provider, but for US Equities, we have a latency of 5-40 milliseconds. A much more significant source of latency is the round trip order times from brokers, which can vary from 100ms to 5 seconds. QuantConnect is not intended for high-frequency trading, but we have integrations to high-speed brokers if you need.

Historical Data

When you request historical data or run warm-up , the amount of historical data you can access depends on the resolution of your data subscriptions. The following table shows the amount of trailing historical data you can access for each data resolution:

Pricing

The QuantConnect data provider serves Forex data for free.

Datasets > QuantConnect > Futures

Introduction

The QuantConnect data provider provides a stream of Futures trades, quotes, and open interest to your trading algorithm during live execution. Live data enables you to make real-time trades and update the value of the securities in your portfolio.

Sourcing

The QuantConnect data provider consolidates Futures market data across the following markets:

• CBOT
• CME
• COMEX
• ICE
• NYMEX

The data is powered by the Chicago Mercantile Exchange (CME).

The data provider doesn't include the CFE market. For Futures.Indices.VIX , use a combination of the QuantConnect and IB data provider. For more details about this option, see Hybrid Data Provider .

Bar Building

We aggregate ticks to build bars.

In live trading, bars are built using the exchange timestamps with microsecond accuracy. This microsecond-by-microsecond processing of the ticks can mean that the individual bars between live trading and backtesting can have slightly different ticks. As a result, it's possible for a tick to be counted in different bars between backtesting and live trading, which can lead to bars having slightly different open, high, low, close, and volume values.

Delivery

Most live trading algorithms run on co-located servers racked in Equinix. Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters.

Live data takes time to travel from the source to your algorithm. The QuantConnect latencies vary depending on the data provider, but for US Equities, we have a latency of 5-40 milliseconds. A much more significant source of latency is the round trip order times from brokers, which can vary from 100ms to 5 seconds. QuantConnect is not intended for high-frequency trading, but we have integrations to high-speed brokers if you need.

Historical Data

When you request historical data or run warm-up , the amount of historical data you can access depends on the resolution of your data subscriptions. The following table shows the amount of trailing historical data you can access for each data resolution:

Pricing

The QuantConnect data provider serves Futures data for free.

Datasets > QuantConnect > Alternative Data

Introduction

The QuantConnect data provider can stream live alternative data into your algorithms to help you make informed trading decisions.

Sourcing

The QuantConnect data provider sources alternative data directly from data vendors . To view all of the integrated data vendors, see the Dataset Market .

Delivery

The delivery schedule of alternative data depends on the specific dataset you're using. We inject the data into your algorithms when the vendor provides the data. For most alternative datasets, the data is updated on a daily or hourly basis. Some datasets, like the Tiingo News Feed or Benzinga News Feed , include a live stream. In these cases, we deliver the data as a live stream to your algorithm.

Most live trading algorithms run on co-located servers racked in Equinix. Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters.

Live data takes time to travel from the source to your algorithm. The latency of the alternative data depends on the specific dataset you're using.

Historical Data

When you request historical data or run warm-up , the amount of historical data you can access depends on the resolution of your data subscriptions. The following table shows the amount of trailing historical data you can access for each data resolution:

Pricing

Refer to the Dataset Market listings.

Datasets > Alpaca

Introduction

Alpaca was founded by Yoshi Yokokawa and Hitoshi Harada in 2015 as a database and machine learning company. In 2018, Alpaca Securities LLC (Alpaca Securities) became a registered US broker-dealer with the Financial Industry Regulatory Authority ( FINRA ) with the mission to "open financial services to everyone on the planet". In 2022, Alpaca Crypto LLC (Alpaca Crypto) became a registered Money Services Business (MSB) with the Financial Crimes Enforcement Network ( FinCEN ). Alpaca provides access to trading Equities, Options, and Crypto for clients in over 30 countries. Alpaca also delivers custody, clearing, execution, and billing on behalf of registered advisors.

The Alpaca data provider serves Equity, Equity Option, and Crypto prices directly from Alpaca's Market Data API . This page explains our integration with their API and its functionality.

Sourcing

The Alpaca data provider sources data directly from Alpaca's Market Data API . If you use this data provider, Alpaca only provides the security price data. QuantConnect Cloud provides the following auxiliary datasets:

• US Equity Security Master
• US Equity Option Universe
• Universe selection datasets
• Non-streaming alternative datasets

Universe Selection

Universe selection is available with the Alpaca data provider.

UniverseSettings.Asynchronous = true;
AddUniverse(FundamentalUniverseSelection);

self.universe_settings.asynchronous = True
self.add_universe(self.fundamental_universe_selection)

The Alpaca data provider can stream data for up to 30 assets on the Basic plan. If your algorithm adds more than the quota, LEAN logs an error message from Alpaca. To increase the quota, purchase the Algo Trader Plus plan from Alpaca.

Bar Building

The data feed is a stream of asset prices collected by WebSockets and distributed to algorithms on the platform.

Alternative Data

Third-party data providers support most alternative datasets, except data that streams real-time intraday data. Streaming datasets, like the Tiingo News Feed and Benzinga News Feed , require the QuantConnect data provider.

Hybrid Data Provider

When you deploy a live algorithm with the Alpaca brokerage , you can use a third-party data provider, the Alpaca data provider, or both. If you use multiple data providers, the order you select them in defines their order of precedence in Lean. For example, if you set QC as the first provider and Alpaca as the second provider, Lean only uses the Alpaca data provider for securities that aren't available from the QC data provider. This configuration makes it possible to use our data provider for Equity universe selection and then place Options trades on the securities in the universe.

Historical Data

The historical data that's available from the Alpaca data provider for history requests and warm-up periods depends on your Alpaca data plan. For more information about each plan, see the Data page on the Alpaca website.

Pricing

The Alpaca data feed is free for Alpaca subscription accounts. The Basic plan has a quota of 30 assets and 200 API calls per minute. The Algo Trader Plus plan has higher quotas. To view the latest prices of the plans, see the Data page on the Alpaca website.

Datasets > Charles Schwab

Introduction

The Charles Schwab Corporation was founded by Charles R. Schwab in 1971. Charles Schwab provides access to trading Equities, Options, Index Options, and other assets for clients with no account or trade minimums, or hidden fees .

The Charles Schwab data provider serves Equity, Equity Option, Index, and Index Option prices directly from Charles Schwab's Trader API . This page explains our integration with their API and its functionality.

Sourcing

The Charles Schwab data provider sources data directly from Charles Schwab's Trader API . If you use this data provider, Charles Schwab only provides the security price data. QuantConnect Cloud provides the following auxiliary datasets:

• US Equity Security Master
• US Equity Option Universe
• US Index Option Universe
• Universe selection datasets
• Non-streaming alternative datasets

Universe Selection

Universe selection is available with the Charles Schwab data provider.

UniverseSettings.Asynchronous = true;
AddUniverse(FundamentalUniverseSelection);

self.universe_settings.asynchronous = True
self.add_universe(self.fundamental_universe_selection)

Bar Building

The data feed is a stream of asset prices collected by WebSockets and distributed to algorithms on the platform.

Alternative Data

Third-party data providers support most alternative datasets, except data that streams real-time intraday data. Streaming datasets, like the Tiingo News Feed and Benzinga News Feed , require the QuantConnect data provider.

Hybrid Data Provider

When you deploy a live algorithm with the Charles Schwab brokerage , you can use a third-party data provider, the Charles Schwab data provider, or both. If you use multiple data providers, the order you select them in defines their order of precedence in Lean. For example, if you set QC as the first provider and Charles Schwab as the second provider, Lean only uses the Charles Schwab data provider for securities that aren't available from the QC data provider. This configuration makes it possible to use our data provider for Equity universe selection and then place Options trades on the securities in the universe.

Pricing

The Charles Schwab data feed is free for Charles Schwab subscription accounts.

Datasets > Interactive Brokers

Introduction

Interactive Brokers (IB) was founded by Thomas Peterffy in 1993 with the goal to "create technology to provide liquidity on better terms. Compete on price, speed, size, diversity of global products and advanced trading tools". IB provides access to trading Equities, ETFs, Options, Futures, Future Options, Forex, CFDs, Gold, Warrants, Bonds, and Mutual Funds for clients in over 200 countries and territories with no minimum deposit. IB also provides paper trading, a trading platform, and educational services.

The Interactive Brokers (IB) data provider serves asset prices from IB's Trader Workstation API . This page explains our integration with their data API and its functionality.

Sourcing

The IB data provider sources data directly from IB's Trader Workstation API . If you use this data provider, IB only provides the security price data. QuantConnect Cloud provides the following auxiliary datasets:

• US Equity Security Master
• US Futures Security Master
• US Equity Option Universe
• US Index Option Universe
• Universe selection datasets
• Non-streaming alternative datasets

Universe Selection

Universe selection is available with the IB data provider.

UniverseSettings.Asynchronous = true;
AddUniverse(FundamentalUniverseSelection);

self.universe_settings.asynchronous = True
self.add_universe(self.fundamental_universe_selection)

The universe selection data comes from our Dataset Market, not the TWS market scanners . Universe selection with the IB data provider occurs around 6-7 AM Eastern Time (ET) on Tuesday to Friday and at 2 AM ET on Sunday. Universe selection data isn't available when the IB servers are closed. To check the IB server status, see the Current System Status page on the IB website.

The IB data provider can stream data for up to 100 assets by default, but IB may let you stream more than 100 assets based on your commissions and equity value. For more information about quotas from IB, see the Market Data Pricing Overview page on the IB website. If your algorithm adds more than the your quota, LEAN logs an error message from IB. To increase the quota, purchase a Quote Booster from IB.

Bar Building

The data is a summarized snapshot of the trades and quotes at roughly 300 milliseconds per snapshot.

Alternative Data

Third-party data providers support most alternative datasets, except data that streams real-time intraday data. Streaming datasets, like the Tiingo News Feed and Benzinga News Feed , require the QuantConnect data provider. The hybrid QuantConnect- IB data provider supports streaming datasets.

Hybrid Data Provider

When you deploy a live algorithm with the IB brokerage , you can use a third-party data provider, the IB data provider, or both. If you use multiple data providers, the order you select them in defines their order of precedence in Lean. For example, if you set QC as the first provider and IB as the second provider, Lean only uses the IB data provider for securities that aren't available from the QC data provider. This configuration makes it possible to use our data provider for Equity universe selection and then place Options trades on the securities in the universe. If you use a third-party data provider, the assets that you subscribe to don't contribute to the IB data limit .

Historical Data

If you get historical data from IB through a history request or a warm-up period , the historical data has the following characteristics:

• Second resolution data is limited to six months of history.
• The historical data excludes delisted Equities and expired Options.
• The historical data excludes expired Futures after two years.

The following quotas are in place for tick and second resolution historical data:

• You can have up to 50 simultaneous requests.
• You can make up to 60 requests within any 10-minute period.

In the preceding quotas, TradeBar and QuoteBar data count as separate requests. For example, if you request TradeBar and QuoteBar data for SPY, it counts as two requests.

For more information about historical data from IB, see Historical Data Limitations in the IB documentation.

Pricing

To use IB data in your algorithms, subscribe to IB market data . We support all of the IB data subscriptions that are related to the securities and markets we support . Members usually subscribe to the following IB market data:

• US Securities Snapshot and Futures Value Bundle
• US Equity and Options Add-On Streaming Bundle
• CFE Enhanced Top of Book (L1 for VIX Futures)
• CME S&P Indexes (L1 for SPX and NDX)
• CBOE Streaming Market Indexes (L1 for VIX Index)

To see the latest prices, check the Market Data Pricing Overview page on the IB website. IB can take up to 24 hours to process subscription requests. So after you subscribe to data, you need to wait 24 hours before you can use it in your algorithms. When you subscribe to data, IB only assigns your data subscription to one of your accounts. If you want to assign the subscription to a different account, for example, a paper trading account instead of a live trading account, then contact IB.

Datasets > Polygon

Introduction

Polygon was founded by Quinton Pike in 2017 with the goal to "break down the barriers that have traditionally limited access to high-quality financial data for all". Polygon provides institutional-grade Equity, Option, Index, Forex, and Crypto data for business and educational purposes.

The Polygon data provider serves asset prices from Polygon. Instead of using the data from QuantConnect or your brokerage, you can use data from Polygon if you're deploying a live algorithm and have an API key. To get an API key, see the API Keys page on the Polygon website. This page explains our integration with Polygon and its functionality.

To view the implementation of the Polygon integration, see the Lean.DataSource.Polygon repository .

QuantConnect Cloud currently only supported streaming Polygon data during live trading. To download Polygon for backtesting, research, and optimizations, use the CLI .

Supported Datasets

The Polygon data provider sources asset price data directly from Polygon. Our integration supports securities from the following asset classes:

• US Equity
• US Equity Options
• US Indices
• US Index Options

To supplement the asset price data from Polygon, QuantConnect Cloud provides the following auxiliary datasets from the Dataset Market:

• US Equity Security Master
• US Equity Option Universe
• US Index Option Universe
• Universe selection datasets
• Non-streaming alternative datasets

For more information about the Polygon data source, see the Polygon API documentation .

Universe Selection

When you trade live on QuantConnect Cloud with the Polygon data provider, QuantConnect provides the universe selection data.

Alternative Data

The alternative datasets in QuantConnect Cloud works seamlessly with the data from Polygon.

Research

The Polygon data provider is not currently supported for research in QuantConnect Cloud.

Backtesting

The Polygon data provider is not currently supported for backtesting in QuantConnect Cloud.

Optimization

The Polygon data provider is not currently supported for optimizations in QuantConnect Cloud.

Live Trading

You must have an available live trading node for each live trading algorithm you deploy.

Follow these steps to deploy a live trading algorithm that uses the Polygon data provider:

1. Open the project that you want to deploy.
2. Click the Deploy Live icon.
3. On the Deploy Live page, click the Brokerage field and then click your brokerage from the drop-down menu.
4. Enter the required brokerage authentication information.

For more information about the required information for each brokerage, see the Deploy Live Algorithms section of your brokerage documentation .

5. In the Data Provider section of the deployment wizard, click Show .
6. Click the Data Provider 1 field and then click Polygon from the drop-down menu.
7. Enter your Polygon API Key.

8. Click Save .
9. (Optional) If your brokerage supports exisiting cash and position holdings , add them.
10. (Optional) Set up notifications .
11. Configure the Automatically restart algorithm setting.

By enabling automatic restarts , the algorithm will use best efforts to restart the algorithm if it fails due to a runtime error. This can help improve the algorithm's resilience to temporary outages such as a brokerage API disconnection.

12. Click Deploy .

Mutiple Data Providers

When you deploy a live algorithm , you can add multiple data providers. If you use multiple data providers, the order you select them in defines their order of precedence in Lean. For example, if you set QuantConnect as the first provider and Polygon as the second provider, Lean only uses the Polygon data provider for securities that aren't available from the QuantConnect data provider. This configuration makes it possible to use QuantConnect data provider for Equity universe selection and use Polygon for Options on the securities in the universe.

Historical Data

The historical data that's available from the Polygon data provider for history requests and warm-up periods depends on your Polygon API package. For more information about each package, see the Simple Pricing page on the Polygon website.

Pricing

To view the prices of the Polygon API packages, see the Simple Pricing page on the Polygon website.

Datasets > Samco

Introduction

Samco was founded by Jimeet Modi in 2015 with a mission of providing retail investors access to sophisticated financial technology that can assist retail investors in creating wealth at a low cost. Samco provides access to India Equities for clients in India with no minimum balance. Samco also provides stock ratings, mutual funds, and a mini-portfolio investment platform.

The Samco data provider serves live asset prices from Samco's Trade API . This page explains our integration with their API and its functionality.

Sourcing

The Samco data provider sources data directly from Samco's Trade API . If you use this data provider, Samco only provides the security price data. QuantConnect Cloud provides the following auxiliary datasets:

• India Equity Security Master
• Non-streaming alternative datasets

Universe Selection

Universe selection isn't available with the Samco data provider.

Bar Building

The Samco data provider consolidates prices and quotes across all of the Indian exchanges. For a complete list of exchange and securities, see the ScripMaster file from the StockNote API documentation.

Alternative Data

Third-party data providers support most alternative datasets, except data that streams real-time intraday data. Streaming datasets, like the Tiingo News Feed and Benzinga News Feed , require the QuantConnect data provider.

Historical Data

For more information about this historical data that's available from the Samco data provider for history requests and warm-up periods , see the Samco Trade API documentation .

Pricing

The Samco data provider is free. To access it, you just need an active Samco account.

Datasets > TradeStation

Introduction

TradeStation was founded by brothers William (Bill) and Rafael (Ralph) Cruz in 1982 as Omega Research, Inc. In 2001, the company converted itself from a trading software company to an online securities brokerage and renamed itself "TradeStation" with the mission to "create the ultimate trading experience". TradeStation provides access to trading Equities, Equity Options, and Futures for clients in over 150 markets, 34 countries, and 27 currencies. TradeStation also delivers custody, clearing, execution, and billing on behalf of registered advisors.

The TradeStation data provider serves Equity, Equity Option, and Futures prices directly from TradeStation's MarketData API . This page explains our integration with their API and its functionality.

Sourcing

The TradeStation data provider sources data directly from TradeStation's MarketData API . If you use this data provider, TradeStation only provides the security price data. QuantConnect Cloud provides the following auxiliary datasets:

• US Equity Security Master
• US Futures Security Master
• US Equity Option Universe
• Universe selection datasets
• Non-streaming alternative datasets

Universe Selection

Universe selection is available with the TradeStation data provider.

UniverseSettings.Asynchronous = true;
AddUniverse(FundamentalUniverseSelection);

self.universe_settings.asynchronous = True
self.add_universe(self.fundamental_universe_selection)

Bar Building

The data feed is a stream of asset prices collected by WebSockets and distributed to algorithms on the platform.

Alternative Data

Third-party data providers support most alternative datasets, except data that streams real-time intraday data. Streaming datasets, like the Tiingo News Feed and Benzinga News Feed , require the QuantConnect data provider.

Hybrid Data Provider

When you deploy a live algorithm with the TradeStation brokerage, you can use a third-party data provider, the TradeStation data provider, or both. If you use multiple data providers, the order you select them in defines their order of precedence in Lean. For example, if you set QC as the first provider and TradeStation as the second provider, Lean only uses the TradeStation data provider for securities that aren't available from the QC data provider. This configuration makes it possible to use our data provider for Equity universe selection and then place Options trades on the securities in the universe.

Historical Data

If you get historical data from TradeStation through a history request or a warm-up period , you can't request tick or second resolution data.

Pricing

To view the latest prices, see the Data page on the TradeStation website.

Datasets > Tradier

Introduction

Tradier was founded by Dan Raju, Peter Laptewicz, Jason Barry, Jeyashree Chidambaram, and Steve Agalloco in 2012 with the goal to "deliver a choice of low-cost, high-value brokerage services to traders". Tradier provides access to trading Equities and Options for clients in over 250 countries and territories with no minimum deposit for cash accounts . Tradier also delivers custody, clearing, execution, and billing on behalf of registered advisors.

The Tradier data provider serves Equity and Option prices directly from Tradier's Market Data API . This page explains our integration with their API and its functionality. If you deploy to the demo environment, Tradier doesn't offer streaming market data due to exchange restrictions related to delayed data, so use the QuantConnect data provider .

Sourcing

The Tradier data provider sources data directly from Tradier's Market Data API . If you use this data provider, Tradier only provides the security price data. QuantConnect Cloud provides the following auxiliary datasets:

• US Equity Security Master
• US Equity Option Universe
• Universe selection datasets
• Non-streaming alternative datasets

For more information about the data source, see the Tradier API documentation .

Universe Selection

Universe selection is available with the Tradier data provider.

UniverseSettings.Asynchronous = true;
AddUniverse(FundamentalUniverseSelection);

self.universe_settings.asynchronous = True
self.add_universe(self.fundamental_universe_selection)

Bar Building

The data feed is a stream of asset prices collected by WebSockets and distributed to algorithms on the platform.

Alternative Data

Third-party data providers support most alternative datasets, except data that streams real-time intraday data. Streaming datasets, like the Tiingo News Feed and Benzinga News Feed , require the QuantConnect data provider.

Hybrid Data Provider

When you deploy a live algorithm with the Tradier brokerage , you can use a third-party data provider, the Tradier data provider, or both. If you use multiple data providers, the order you select them in defines their order of precedence in Lean. For example, if you set QC as the first provider and Tradier as the second provider, Lean only uses the Tradier data provider for securities that aren't available from the QC data provider. This configuration makes it possible to use our data provider for Equity universe selection and then place Options trades on the securities in the universe.

Historical Data

Historical data isn't available for expired Options from the Tradier data provider. For more information about the data that's available, see Get Historical Pricing in the Tradier documentation.

Pricing

The Tradier data feed is free for Tradier subscription accounts. If you have a free Tradier account, you may have to pay inactivity and maintenance fees. If you have less than $2,000 in total account value and less than 2 executed trades in 1 year, the inactivity fee is $50. If you have less than 2 executed trades per month, the international account monthly maintenance fee is $20. To view the latest prices, see the Pricing page on the Tradier website.

Datasets > Zerodha

Introduction

Zerodha was founded by Nithin Kamath in 2010 with the goal to break all barriers that traders and investors face in India in terms of cost, support, and technology. Zerodha provides access to India Equities for clients in India with no minimum balance required. Zerodha also provides a mutual fund investment platform and an interactive portfolio dashboard.

The Zerodha data provider serves asset prices from Zerodha's Kite Connect API . This page explains our integration with their API and its functionality.

Sourcing

The Zerodha data provider sources data directly from Zerodha's Kite Connect API . If you use this data provider, Zerodha only provides the security price data. QuantConnect Cloud provides the following auxiliary datasets:

• India Equity Security Master
• Non-streaming alternative datasets

For more information about the data source, see the Kite Connect API documentation .

Universe Selection

Universe selection isn't available with the Zerodha data provider.

Bar Building

The data provider consolidates prices and quotes across all of the Indian exchanges.

Alternative Data

Third-party data providers support most alternative datasets, except data that streams real-time intraday data. Streaming datasets, like the Tiingo News Feed and Benzinga News Feed , require the QuantConnect data provider.

Historical Data

The historical data that's available from the Zerodha data provider for history requests and warm-up periods is in a minute, hourly, or daily resolution. For more information about the data that's available, see Historical candle data in the Kite Connect 3 API documentation.

Pricing

The Zerodha data feed costs ₹2000/month for retail users. To view the latest prices, see the What are the charges for KITE APIs? page on the Zerodha website.

Live Trading

Live Trading > Getting Started

Introduction

A live algorithm is an algorithm that trades in real-time with real market data. QuantConnect enables you to run your algorithms in live mode with real-time market data. Deploy your algorithms using QuantConnect because our infrastructure is battle-tested. The algorithms that our members create are run on co-located servers and the trading infrastructure is maintained at all times by our team of engineers. It's common for members to achieve 6-months of uptime with no interruptions.

Deploy Live Algorithms

The following video demonstrates how to deploy live paper trading algorithms:

You must have an available live trading node for each live trading algorithm you deploy.

Follow these steps to deploy a live paper trading algorithm:

1. Open the project that you want to deploy.
2. Click the Deploy Live icon.
3. On the Deploy Live page, click the Brokerage field and then click Paper Trading from the drop-down menu.
4. Click the Node field and then click the live trading node that you want to use from the drop-down menu.
5. (Optional) In the Data Provider section, click Show and change the data provider or add additional providers.
6. (Optional) Follow these steps to start the algorithm with existing cash holdings ( see video ):
1. In the Algorithm Cash State section, click Show .
2. Click Add Currency .
3. Enter the currency ticker (for example, USD or BTC) and a quantity.
7. (Optional) Follow these steps to start the algorithm with existing position holdings ( see video ):
1. In the Algorithm Holdings State section, click Show .
2. Click Add Holding .
3. Enter the symbol ID, symbol, quantity, and average price.
8. (Optional) Set up notifications .
9. Configure the Automatically restart algorithm setting.

By enabling automatic restarts , the algorithm will use best efforts to restart the algorithm if it fails due to a runtime error. This can help improve the algorithm's resilience to temporary outages such as a brokerage API disconnection.

10. Click Deploy .

The deployment process can take up to 5 minutes. When the algorithm deploys, the live results page displays. If you know your brokerage positions before you deployed, you can verify they have been loaded properly by checking your equity value in the runtime statistics, your cashbook holdings, and your position holdings.

To deploy a live algorithm with a different brokerage, see the Deploy Live Algorithms section of the brokerage integration documentation .

Stop Live Algorithms

The live trading results page has a Stop button to immediately stop your algorithm from executing. When you stop a live algorithm, your portfolio holdings are retained. Stop your algorithm if you want to perform any of the following actions:

• Update your project's code files
• Upgrade the live trading node
• Update the settings you entered into the deployment wizard
• Place manual orders through your brokerage account instead of the web IDE

Furthermore, if you receive new securities in your portfolio because of a reverse merger, you also need to stop and redeploy the algorithm.

LEAN actively terminates live algorithms when it detects interference outside of the algorithm's control to avoid conflicting race conditions between the owner of the account and the algorithm, so avoid manipulating your brokerage account and placing manual orders on your brokerage account while your algorithm is running. If you need to adjust your brokerage account holdings, stop the algorithm, manually place your trades, and then redeploy the algorithm.

Follow these steps to stop your algorithm:

1. Open your algorithm's live results page .
2. Click Stop .
3. Click Stop again.

Liquidate Live Algorithms

The live results page has a Liquidate button that acts as a "kill switch" to sell all of your portfolio holdings. If your algorithm has a bug in it that caused it to purchase a lot of securities that you didn't want, this button let's you easily liquidate your portfolio instead of placing many manual trades. When you click the Liquidate button, if the market is open for an asset you hold, the algorithm liquidates it with market orders. If the market is not open, the algorithm places market on open orders. After the algorithm submits the liquidation orders, it stops executing.

Follow these steps to liquidate your positions:

1. Open your algorithm's live results page .
2. Click Liquidate .
3. Click Liquidate again.

Update Live Algorithms

If you need to adjust your algorithm's project files or parameter values , stop your algorithm, make your changes, and then redeploy your algorithm. You can't adjust your algorithm's code or parameter values while your algorithm executes.

When you stop and redeploy a live algorithm, your project's live results is retained between the deployments. To clear the live results history, clone the project and then redeploy the cloned version of the project.

To update parameters in live mode, add a Schedule Event that downloads a remote file and uses its contents to update the parameter values.

private Dictionary _parameters = new();
public override void Initialize()
{
    if (LiveMode)
    {
        Schedule.On(
            DateRules.EveryDay(),
            TimeRules.Every(TimeSpan.FromMinutes(1)),
            ()=>
            {
                var content = Download(urlToRemoteFile);
                // Convert content to _parameters
            });
    }
}

def initialize(self):
    self.parameters = { }
    if self.live_mode:
        def download_parameters():
            content = self.download(url_to_remote_file)
            # Convert content to self.parameters

        self.schedule.on(self.date_rules.every_day(), self.time_rules.every(timedelta(minutes=1)), download_parameters)

Clear Live Algorithms History

When you stop and redeploy a live algorithm, your project's live results is retained between the deployments. To clear the live results history, clone the project and then redeploy the cloned version of the project.

On-Premise Live Algorithms

For information about on-premise live trading with Local Platform , see Getting Started .

Get Deployment Id

To get the live deployment Id, open the log file and enter "Launching analysis for" into the search bar. The log file shows all of the live deployment Ids for the project. An example deployment Id is L-6bf91128391608d0728ff90b81bfca41. If you have deployed the project multiple times, use the most recent deployment Id in the log file.

Automate Deployments

If you have multiple deployments, use a notebook in the Research Enviroment to programmatically deploy, stop or liquidate algorithms.

Live Trading > Brokerages

Live Trading > Brokerages > QuantConnect Paper Trading

Introduction

QuantConnect enables you to run your algorithms in live mode with real-time market data.

QuantConnect Paper Trading lets you run live, real-time data into your algorithm but execute trades using fictional capital. Instead of your orders being routed to an exchange when you're paper trading, your order fills are simulated. Use paper trading to test your algorithm without risking real money and to ensure your backtest wasn't overfit before deploying with real money. You can use the paper trading brokerage without needing to sign up for a real brokerage account. If you don't set a brokerage model in your algorithm with the SetBrokerageModel set_brokerage_model method, the paper trading brokerage uses the DefaultBrokerageModel to simulate trades.

To view the implementation of the QuantConnect Paper Trading brokerage, see PaperBrokerage.cs in the LEAN GitHub repository. To view the implementation of the backtesting brokerage, see BacktestingBrokerage.cs in the LEAN GitHub repository.

Account Types

The QuantConnect Paper Trading brokerage supports cash and margin accounts. To set the account type in an algorithm, see the paper trading brokerage model documentation .

If you pass a different BrokerageName to the SetBrokerageModel set_brokerage_model method, the new brokerage model defines the account types that are available.

Asset Classes

The QuantConnect Paper Trading brokerage supports the following asset classes:

• US Equities
• Crypto
• Forex
• CFD
• Futures
• Future Options

If you set the brokerage model to a model other than the DefaultBrokerageModel , the new brokerage model defines the asset classes you can trade.

Data Feeds

We can only provide paper trading on the assets for which we have a live data provider .

Orders

The following sections describe how the DefaultBrokerageModel handles orders. If you set the brokerage model to a different model, the new brokerage model defines how orders are handled.

Order Types

The following table describes the available order types for each asset class that the DefaultBrokerageModel supports:

Order Type	US Equity	Crypto	Crypto Futures	Forex	CFD	Futures	Futures Options
Market
Limit
Limit if touched
Stop market
Stop limit
Market on open
Market on close
Combo market
Combo limit
Combo leg limit
Exercise Option

In live trading, Option orders require a third-party data provider that supports Options. To view the data providers that support Options, see Datasets .

Time In Force

The DefaultBrokerageModel supports the following TimeInForce instructions:

• Day DAY
• GoodTilCanceled good_til_canceled
• GoodTilDate good_til_date

Updates

The DefaultBrokerageModel supports order updates .

Handling Splits

If you're using raw data normalization and you have active orders with a limit, stop, or trigger price in the market for a US Equity when a stock split occurs, the following properties of your orders automatically adjust to reflect the stock split:

• Quantity
• Limit price
• Stop price
• Trigger price

Fees

The following table shows the fees that the DefaultBrokerageModel charges for each of the supported asset classes:

There is no fee to exercise Option contracts.

If you set the brokerage model to a model other than the DefaultBrokerageModel , the new brokerage model defines the order fees.

To see the fee models that the DefaultBrokerageModel uses, see Fees .

Margin

We model buying power and margin calls to ensure your algorithm stays within the margin requirements. If you set the brokerage model to a different model, the new brokerage model defines how margin is modeled. If you have more than $25,000 in your brokerage account, you can use the PatternDayTradingMarginModel to make use of the 4x intraday leverage and 2x overnight leverage available on most brokerages from the PDT rule .

Slippage

Orders through the DefaultBrokerageModel do not experience slippage in backtests or paper trading. For more information about the slippage model the DefaultBrokerageModel uses, see Slippage .

Fills

The DefaultBrokerageModel fills market orders immediately and completely. When available, bid and ask spread will be used for the fill prices.

To view how we model realistic order fills, see Fills .

Settlements

If you trade with a margin account, trades settle immediately

To view how we model settlement for paper trades, see Settlement .

Brokerage Models

The QuantConnect Paper Trading brokerage uses the DefaultBrokerageModel by default, but you can use any of the brokerage models .

Deposits and Withdrawals

The QuantConnect Paper Trading brokerage supports deposits and withdrawals.

// Deposit 100 units of account currency and withdraw 1 ETH.
Porfolio.CashBook.Add(AccountCurrency, 100);
Porfolio.CashBook.Add("ETH", -1);

# Deposit 100 units of account currency and withdraw 1 ETH.
self.porfolio.cash_book.add(self.account_currency, 100)
self.porfolio.cash_book.add("ETH", -1)

Demo Algorithm

The following algorithm demonstrates the functionality of the DefaultBrokerageModel :

Deploy Live Algorithms

You must have an available live trading node for each live trading algorithm you deploy.

Follow these steps to deploy a live paper trading algorithm:

1. Open the project that you want to deploy.
2. Click the Deploy Live icon.
3. On the Deploy Live page, click the Brokerage field and then click Paper Trading from the drop-down menu.
4. Click the Node field and then click the live trading node that you want to use from the drop-down menu.
5. (Optional) In the Data Provider section, click Show and change the data provider or add additional providers.
6. (Optional) Follow these steps to start the algorithm with existing cash holdings ( see video ):
1. In the Algorithm Cash State section, click Show .
2. Click Add Currency .
3. Enter the currency ticker (for example, USD or BTC) and a quantity.
7. (Optional) Follow these steps to start the algorithm with existing position holdings ( see video ):
1. In the Algorithm Holdings State section, click Show .
2. Click Add Holding .
3. Enter the symbol ID, symbol, quantity, and average price.
8. (Optional) Set up notifications .
9. Configure the Automatically restart algorithm setting.

By enabling automatic restarts , the algorithm will use best efforts to restart the algorithm if it fails due to a runtime error. This can help improve the algorithm's resilience to temporary outages such as a brokerage API disconnection.

10. Click Deploy .

The deployment process can take up to 5 minutes. When the algorithm deploys, the live results page displays. If you know your brokerage positions before you deployed, you can verify they have been loaded properly by checking your equity value in the runtime statistics, your cashbook holdings, and your position holdings.

Live Trading > Brokerages > Interactive Brokers

Introduction

QuantConnect enables you to run your algorithms in live mode with real-time market data.

Interactive Brokers (IB) was founded by Thomas Peterffy in 1993 with the goal to "create technology to provide liquidity on better terms. Compete on price, speed, size, diversity of global products and advanced trading tools". IB provides access to trading Equities, ETFs, Options, Futures, Future Options, Forex, CFDs, Gold, Warrants, Bonds, and Mutual Funds for clients in over 200 countries and territories with no minimum deposit. IB also provides paper trading, a trading platform, and educational services.

To view the implementation of the IB brokerage integration, see the Lean.Brokerages.InteractiveBrokers repository .

Account Types

The IB API does not support the IBKR LITE plan. You need an IBKR PRO plan. Individual and Financial Advisor (FA) accounts are available.

Individual Accounts

IB supports cash and margin accounts. To set the account type in an algorithm, see the IB brokerage model documentation .

FA Accounts

IB supports FA accounts for Trading Firm and Institution organizations. FA accounts enable certified professionals to use a single trading algorithm to manage several client accounts. If your account code starts with F, FA, or I, then you have an FA account. For more information about FA accounts, see Financial Advisors .

Create an Account

You need to open an IBKR Pro account to deploy algorithms with IB. The IB API does not support IBKR Lite accounts. To create an IB account, see the Open an Account page on the IB website.

You need to activate IBKR Mobile Authentication (IB Key) to deploy live algorithms with your brokerage account. After you open your account, follow the installation and activation instructions on the IB website.

Paper Trading

IB supports paper trading. Follow the Opening a Paper Trading Account page in the IB documentation to set up your paper trading account.

If you want to use IB market data and trade with your paper trading account, follow these steps:

1. Log in to the IB Client Portal.
2. In the top-right corner, click the person icon and then click Settings .
3. In the Account Configuration section, click Paper Trading Account .
4. Click Yes .
5. Click Save .

The IB paper trading environment simulates most aspects of a production Trader Workstation account, but you may encounter some differences due to its construction as a simulator with no execution or clearing abilities.

Insured Bank Deposit Sweep Program

LEAN doesn't support IB accounts in the Insured Bank Deposit Sweep Program because when LEAN reads your account balances, it includes cash that's in the FDIC Sweep Account Cash, which isn't tradable. For example, if your account has $150K USD of cash, only $100K may be available to trade if $50K is in FDIC Sweep Account Cash.

Dividend Election

Dividend election is an option where you can elect how you wish to receive your dividends for stocks and mutual funds. You must turn automatic dividend election off to receive them in cash. Reinvestment can change the quantity of shares you own to fractional shares, and LEAN doesn't support fractional trading . For example, if your account has 1270.8604 shares of TLT after dividend reinvestment, you cannot liquidate the position.

Asset Classes

Our Interactive Brokers integration supports the following asset classes:

• US Equities
• Equity Options
• Forex
• Futures
• Future Options
• Indices
• Index Options
• CFD

You may not be able to trade all assets with IB. For example, if you live in the EU, you can't trade US ETFs. You can trade the CFD equivalent . Check with your local regulators to know which assets you are allowed to trade. You may need to adjust settings in your brokerage account to live trade some assets.

Data Providers

You might need to purchase an IB market data subscription for your trading. For more information about live data providers, see Datasets .

Orders

We model the IB API by supporting several order types, order properties, and order updates. When you deploy live algorithms, you can place manual orders through the IDE.

Order Types

The following table describes the order types that our IB integration supports: supports. For specific details about each order type, refer to the IB documentation.

The following table describes the available order types for each asset class that IB supports:

Order Type	US Equity	Equity Options	Forex	Futures	Futures Options	Index Options	CFD
Market
Limit
Limit if touched
Stop market
Stopl imit
Trailing stop
Market on open
Market on close
Combo market
Combo limit
Combo leg limit
Exercise Option		Not supported for cash-settled Options

Order Properties

We model custom order properties from the IB API. The following table describes the members of the InteractiveBrokersOrderProperties object that you can set to customize order execution. The table does not include the methods for FA accounts .

Updates

We model the IB API by supporting order updates .

Financial Advisor Group Orders

To place FA group orders, see Financial Advisors .

Fractional Trading

The IB API and FIX/CTCI don't support fractional trading .

Handling Splits

If you're using raw data normalization and you have active orders with a limit, stop, or trigger price in the market for a US Equity when a stock split occurs, the following properties of your orders automatically adjust to reflect the stock split:

• Quantity
• Limit price
• Stop price
• Trigger price

Fill Time

IB has a 400 millisecond fill time for live orders.

Brokerage Liquidations

When IB liquidates part of your position, you receive an order event that contains the Brokerage Liquidation message.

Fees

To view the IB trading fees, see the Commissions page on the IB website. To view how we model their fees, see Fees .

Margin

We model buying power and margin calls to ensure your algorithm stays within the margin requirements. If you have more than $25,000 in your brokerage account, you can use the PatternDayTradingMarginModel to make use of the 4x intraday leverage and 2x overnight leverage available on most brokerages from the PDT rule .

Slippage

Orders through IB do not experience slippage in backtests. In IB paper trading and live trading, your orders may experience slippage.

To view how we model IB slippage, see Slippage .

Fills

We fill market orders immediately and completely in backtests. In IB paper trading and live trading, if the quantity of your market orders exceeds the quantity available at the top of the order book, your orders are filled according to what is available in the order book.

To view how we model IB order fills, see Fills .

Settlements

If you trade with a margin account, trades settle immediately

To view how we model settlement for IB trades, see Settlement .

Security and Stability

Note the following security and stability aspects of our IB integration.

Account Credentials

When you deploy live algorithms with IB , we don't save your brokerage account credentials.

API Outages

We call the IB API to place live trades. Sometimes the API may be down. Check the IB status page to see if the API is currently working.

Connections

By default, IB only supports one connection at a time to your account. If you interfere with your brokerage account while an algorithm is connected to it, the algorithm may stop executing. If you deploy a live running algorithm with your IB account and want to open Trader Workstation (TWS) with the same IB account, create a second user on your IB account and log in to TWS with the new user credentials. To run more than one algorithm with IB, open an IB subaccount for each additional algorithm.

If you can't log in to TWS with your credentials, contact IB. If you can log in to TWS but can't log in to the deployment wizard, contact us and provide the algorithm ID and deployment ID.

SMS 2FA

Our IB integration doesn't support Two-Factor Authentication (2FA) via SMS or the Online Security Code Card. Use the IB Key Security via IBKR Mobile instead.

System Resets

You'll receive a notification on your IB Key device every Sunday to re-authenticate the connection between IB and your live algorithm. When you deploy your algorithm , you can select a time on Sunday to receive the notification. If you don't re-authenticate before the timeout period, your algorithm quits executing. Ensure your IB Key device has sufficient battery for the time you expect to receive the notification. If you don't receive a notification, see I am not receiving IBKR Mobile notifications on the IB website.

Deposits and Withdrawals

You can deposit and withdraw cash from your brokerage account while you run an algorithm that's connected to the account. We sync the algorithm's cash holdings with the cash holdings in your brokerage account every day at 7:45 AM Eastern Time (ET).

Demo Algorithm

The following algorithm demonstrates the functionality of the IB brokerage:

Deploy Live Algorithms

You must have an available live trading node for each live trading algorithm you deploy.

Follow these steps to deploy a live algorithm:

1. Open the project you want to deploy.
2. Click the Deploy Live icon.
3. On the Deploy Live page, click the Brokerage field and then click Interactive Brokers from the drop-down menu.
4. Enter your IB user name, ID, and password.
if ($localPlatform) { include(DOCS_RESOURCES."/brokerages/interactive-brokers/paper-trading-data-feeds.html"); } ?>

Your account details are not saved on QuantConnect.

5. In the Weekly Restart UTC field, enter the Coordinated Universal Time (UTC) time of when you want to receive notifications on Sundays to re-authenticate your account connection.

For example, 4 PM UTC is equivalent to 11 AM Eastern Standard Time, 12 PM Eastern Daylight Time, 8 AM Pacific Standard Time, and 9 AM Pacific Daylight Time. To convert from UTC to a different time zone, see the UTC Time Zone Converter on the UTC Time website.

If your IB account has 2FA enabled, you receive a notification on your IB Key device every Sunday to re-authenticate the connection between IB and your live algorithm. If you don't re-authenticate before the timeout period, your algorithm quits executing.

6. Click the Node field and then click the live trading node that you want to use from the drop-down menu.
7. (Optional) In the Data Provider section, click Show and change the data provider or add additional providers.

In most cases, we suggest using both the QC and IB data providers .

If you use IB data provider and trade with a paper trading account, you need to share the market data subscription with your paper trading account. For instructions on sharing market data subscription, see Account Types .

8. (Optional) Set up notifications .
9. Configure the Automatically restart algorithm setting.

By enabling automatic restarts , the algorithm will use best efforts to restart the algorithm if it fails due to a runtime error. This can help improve the algorithm's resilience to temporary outages such as a brokerage API disconnection.

10. Click Deploy .
11. If your IB account has 2FA enabled, tap the notification on your IB Key device and then enter your pin.

The deployment process can take up to 5 minutes. When the algorithm deploys, the live results page displays. If you know your brokerage positions before you deployed, you can verify they have been loaded properly by checking your equity value in the runtime statistics, your cashbook holdings, and your position holdings.

Troubleshooting

The following table describes errors you may see when deploying to IB:

To view the description of less common errors, see Error Codes in the TWS API Documentation. If you need further support, open a new support ticker and add the live deployment with the error.

Live Trading > Brokerages > TradeStation

Introduction

QuantConnect enables you to run your algorithms in live mode with real-time market data.

TradeStation was founded by brothers William (Bill) and Rafael (Ralph) Cruz in 1982 as Omega Research, Inc. In 2001, the company converted itself from a trading software company to an online securities brokerage and renamed itself "TradeStation" with the mission to "create the ultimate trading experience". TradeStation provides access to trading Equities, Equity Options, and Futures for clients in over 150 markets, 34 countries, and 27 currencies. TradeStation also delivers custody, clearing, execution, and billing on behalf of registered advisors.

To view the implementation of the TradeStation brokerage integration, see the Lean.Brokerages.TradeStation repository .

Account Types

TradeStation supports cash and margin accounts. To set the account type in an algorithm, see the TradeStation brokerage model documentation .

Create an Account

To create a TradeStation account, follow the account creation wizard on the TradeStation website.

Paper Trading

TradeStation supports paper trading through their trading simulator after you fund your brokerage account.

Asset Classes

Our TradeStation integration supports trading the following asset classes:

• US Equities
• Equity Options
• Futures
• Index
• Index Options

You may not be able to trade all assets with TradeStation . For example, if you live in the EU, you can't trade US ETFs. Check with your local regulators to know which assets you are allowed to trade. You may need to adjust settings in your brokerage account to live trade some assets.

Data Providers

You might need to purchase a TradeStation market data subscription for your trading. For more information about live data providers, see Datasets .

Orders

We model the TradeStation API by supporting several order types, order properties, and order updates. When you deploy live algorithms, you can place manual orders through the IDE.

Order Types

The following table describes the available order types for each asset class that our TradeStation integration supports:

Order Type	Equity	Equity Options	Index Options	Futures
Market
Limit
Stop market
Stop limit
Trailing stop
Market on Open
Market on Close
Combo market
Combo limit

Order Properties

We model the TradeStation API. The following table describes the members of the TradeStationOrderProperties object that you can set to customize order execution.